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+{"source": "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.", "target": ["我们设计了自适应损失缩放来改善混合精度训练，超过了最先进的结果。", "在混合精度训练的反向传播过程中，提出了一种自适应损失缩放方法，该方法自动决定缩放率以减少下溢。", "作者提出了一种在FP16精度下训练模型的方法，该方法采用了一种更精细的方式来同时自动减少每层的下溢。"]}
+{"source": "Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities.This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors.We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks.Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization.We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.", "target": ["我们提出了一种新的方法，利用前馈深度神经网络来学习预测具有未知排列组合和基数的集合。", "基于深度网络的集合预测问题，学习不可观察的排列变数分布的表述。"]}
+{"source": "Foveation is an important part of human vision, and a number of deep networks have also used foveation.However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods.Here we define several such methods, and compare their performance on ImageNet recognition with a Densenet-121 network.The best variable-resolution method slightly outperforms uniform downsampling.Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks.", "target": ["我们比较了统一降采样的图像和三种不同凹陷方案的物体识别性能。"]}
+{"source": "We explore the concept of co-design in the context of neural network verification.Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily.To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task.We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones.Then, improving ReLU stability leads to an additional 4-13x speedup in verification times.An important feature of our methodology is its \"universality,\" in the sense that it can be used with a broad range of training procedures and verification approaches.", "target": ["我们开发了训练深度神经模型的方法，这些模型对对抗性扰动具有强化，而且其强化性明显更容易验证。", "本文介绍了几种对普通ReLU网络进行正则化的方法，以使对抗性强化、可证明的对抗性强化和验证速度达到最佳。", "本文提出了训练稳定的神经网络的方法，可以更快地验证，使用修剪方法来鼓励权重的稀疏性和正则化来鼓励ReLU稳定性。"]}
+{"source": "Batch Normalization (BatchNorm) has shown to be effective for improving and accelerating the training of deep neural networks.However, recently it has been shown that it is also vulnerable to adversarial perturbations.In this work, we aim to investigate the cause of adversarial vulnerability of the BatchNorm.We hypothesize that the use of different normalization statistics during training and inference (mini-batch statistics for training and moving average of these values at inference) is the main cause of this adversarial vulnerability in the BatchNorm layer.We empirically proved this by experiments on various neural network architectures and datasets.Furthermore, we introduce Robust Normalization (RobustNorm) and experimentally show that it is not only resilient to adversarial perturbation but also inherit the benefits of BatchNorm.", "target": ["调查BatchNorm如何导致对抗脆弱性以及如何避免它。", "本文讨论了BatchNorm中对对抗性扰动的脆弱性，并提出了一个名为RobustNorm的替代方案，使用最小-最大重定比例代替正规化。", "本文研究了BatchNorm的脆弱性背后的原因，并提出了Robust Normalization，一种在各种攻击方法下都能取得明显效果的正规化方法。"]}
+{"source": "Electronic Health Records (EHR) comprise of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality which become the major obstacles in drawing reliable downstream outcome.Despite greatly numbers of imputation methods are being proposed to tackle these issues, most of the existing methods ignore correlated features or temporal dynamics and entirely put aside the uncertainty.In particular, since the missing values estimates have the risk of being imprecise, it motivates us to pay attention to reliable and less certain information differently.In this work, we propose a novel variational-recurrent imputation network (V-RIN), which unified imputation and prediction network, by taking into account the correlated features, temporal dynamics, and further utilizing the uncertainty to alleviate the risk of biased missing values estimates.Specifically, we leverage the deep generative model to estimate the missing values based on the distribution among variables and a recurrent imputation network to exploit the temporal relations in conjunction with utilization of the uncertainty.We validated the effectiveness of our proposed model with publicly available real-world EHR dataset, PhysioNet Challenge 2012, and compared the results with other state-of-the-art competing methods in the literature.", "target": ["我们的变异-循环网络（V-RIN）考虑到了相关的特征、时间动态，并进一步利用不确定性来减轻有误差的缺失值估计的风险。", "一个缺失数据填补网络，将相关性、时间关系和数据的不确定性纳入EHRs的数据稀少问题，在死亡率分类任务中产生了更高的AUC。", "该论文提出了一种结合VAE和不确定性意识的GRU的方法，用于连续缺失数据的填补和结果预测。"]}
+{"source": "Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity.Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models.However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account.We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized.The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution.Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates.Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy.", "target": ["基于理论分析而非启发式方法的神经网络定点量化的自适应方法。", "提出了一种量化神经网络的方法，允许根据权重的重要性对其进行不同精度的量化，同时考虑到了损失。", "本文提出了一种将神经网络的权重量化的技术，其位深/精度在每个参数的基础上变化。"]}
+{"source": "We study the problem of learning permutation invariant representations that can capture containment relations.We propose training a model on a novel task: predicting the size of the symmetric difference between pairs of multisets, sets which may contain multiple copies of the same object.With motivation from fuzzy set theory, we formulate both multiset representations and how to predict symmetric difference sizes given these representations.We model multiset elements as vectors on the standard simplex and multisets as the summations of such vectors, and we predict symmetric difference as the l1-distance between multiset representations.We demonstrate that our representations more effectively predict the sizes of symmetric differences than DeepSets-based approaches with unconstrained object representations.Furthermore, we demonstrate that the model learns meaningful representations, mapping objects of different classes to different standard basis vectors.", "target": ["基于模糊集合理论，我们提出了一个模型，该模型只给定多集对之间的对称性差异的大小，学习这种多集及其元素的表示。", "本文提出了一个集合学习的新任务，即预测多集合之间对称差的大小，并给出了一种基于模糊集合理论的解决方法。"]}
+{"source": "It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system).In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold a relevant information.  Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice.This is primarily due to the heavy cognitive loads required for human agents to reason and report this high dimensional information.Consider the example where we are interested in building an image classifier via first collecting a certain category of  high-dimensional image data.While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \\sim p(x)$ from agents.This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents.The challenge to do so is to design an incentive-compatible score function to score each reported sample to induce truthful reports, instead of an arbitrary or even adversarial one.We show that with accurate estimation of a certain $f$-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.We then present an efficient estimator with theoretical guarantee via studying the variational forms of $f$-divergence function.Our work complements the literature of information elicitation via introducing the problem of \\emph{sample elicitation}.  We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.", "target": ["本文提出了一种深度学习辅助的方法，从自利的代理人那里获得可信的样本。", "作者针对从代理人那里引出复杂分布的可信样本的问题提出了一个样本引出框架，提出深度神经框架可以在这个框架中应用，并将样本引出和f-GAN联系起来。", "本文研究了样本诱导问题，提出了一种深度学习方法，该方法依赖于f-分歧的双重表达，该表达在一组函数t上写为最大值。"]}
+{"source": "The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks.However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence.To address this challenge, we introduce a general end-to-end graph-to-sequence neural encoder-decoder architecture that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs.Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.", "target": ["以注意力为基础的神经网络的图形到序列学习", "一个Graph2seq架构，将混合了GGNN和GCN组件的图形编码器与注意力序列编码器结合在一起，并显示出比基线有所改进。", "这项工作提出了一个端到端的图形编码器到序列解码器模型，中间有一个关注机制。"]}
+{"source": "We address the problem of learning to discover 3D parts for objects in unseen categories.Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches.Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion.At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories.On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples.Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.", "target": ["一个用于三维物体部分分割的零样本分割框架。将分割建模为一个决策过程，并作为一个上下文赌博机问题来解决。", "一种将物体的三维点云分割成组合零件的方法，重点是将部件分组归纳到训练期间未见过的新物体类别中，相对于基线显示出强大的性能。", "本文提出了一种在物体点云中进行部件分割的方法。"]}
+{"source": "This paper presents the ballistic graph neural network.Ballistic graph neural network tackles the weight distribution from a transportation perspective and has many different properties comparing to the traditional graph neural network pipeline.The ballistic graph neural network does not require to calculate any eigenvalue.The filters propagate exponentially faster($\\sigma^2 \\sim T^2$) comparing to traditional graph neural network($\\sigma^2 \\sim T$).We use a perturbed coin operator to perturb and optimize the diffusion rate.Our results show that by selecting the diffusion speed, the network can reach a similar accuracy with fewer parameters.We also show the perturbed filters act as better representations comparing to pure ballistic ones.We provide a new perspective of training graph neural network, by adjusting the diffusion rate, the neural network's performance can be improved.", "target": ["关于如何根据扩散特性收集节点之间的关联性的新观点。", "一种新的图神经网络的扩散操作，不需要计算特征值，与传统的图神经网络相比，可以以指数级速度传播。", "本文提出通过引入弹道行走来应对扩散速度的问题。"]}
+{"source": "In this paper, we propose a \\textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \\citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources.Empirically, we consider two sources of weak supervision signals, unsupervised ranking functions and semantic feature similarities.We train a BERT-based passage-ranking model (which achieves new state-of-the-art performances on two benchmark datasets with full supervision) in our weak supervision framework.Without using ground-truth training labels, BERT-PR models outperform BM25 baseline by a large margin on all three datasets and even beat the previous state-of-the-art results with full supervision on two of datasets.", "target": ["我们提出了一个基于数据编程框架的排名任务的弱监督训练管道，其中我们训练一个基于BERT的排名模型并建立新的SOTA。", "作者提出了一个BERT和弱监督框架的组合来解决通道排名的问题，获得了比完全监督的最先进的结果。"]}
+{"source": "We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.We demonstrate a very universal Frequency Principle (F-Principle) --- DNNs often fit target functions from low to high frequencies --- on high-dimensional benchmark datasets, such as MNIST/CIFAR10, and deep networks, such as VGG16.This F-Principle of DNNs is opposite to the learning behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibits faster convergence for higher frequencies, for various scientific computing problems.With a naive theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions.The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function.This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.", "target": ["在实际问题中，我们发现DNN在训练过程中经常从低频到高频地拟合目标函数。", "本文分析了神经网络在傅里叶域的损失，发现DNN倾向于在高频成分之前学习低频成分。", "本文通过傅里叶分析研究了NN的训练过程，得出结论：NN在学习高频成分之前，先学习低频成分。"]}
+{"source": "The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties.Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization.In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph.Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule.We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.", "target": ["我们提出了一个多解析度、分层耦合的编码器-解码器，用于图到图的翻译。", "用化学子结构作为构件生成分子图的分层图-图转换模型，该模型是完全自回归的，并能学习连贯的多解析度表征，性能优于以前的模型。", "作者提出了一种用于生成新型有机分子的分层图到图的翻译方法。"]}
+{"source": "Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping.Even though some combinations of transformations might never appear (e.g. an upright face with a horizontal nose), current equivariant architectures consider the set of all possible transformations in a transformation group when learning feature representations.Contrarily, the human visual system is able to attend to the set of relevant transformations occurring in the environment and utilizes this information to assist and improve object recognition.Based on this observation, we modify conventional equivariant feature mappings such that they are able to attend to the set of co-occurring transformations in data and generalize this notion to act on groups consisting of multiple symmetries.We show that our proposed co-attentive equivariant neural networks consistently outperform conventional rotation equivariant and rotation & reflection equivariant neural networks on rotated MNIST and CIFAR-10.", "target": ["我们利用注意力将等价神经网络限制在数据中的一组或共同发生的转变。", "本文将注意力与群的等价性结合起来，特别是研究旋转、平移和翻转的p4m群，并推导出一种不破坏等价性的自我注意力的形式。", "作者提出了一种旋转等值神经网络的自我注意机制，它比常规的旋转等值网络提高了分类性能。"]}
+{"source": "The fast generation and refinement of protein backbones would constitute a major advancement to current methodology for the design and development of de novo proteins.In this study, we train Generative Adversarial Networks (GANs) to generate fixed-length full-atom protein backbones, with the goal of sampling from the distribution of realistic 3-D backbone fragments.We represent protein structures by pairwise distances between all backbone atoms, and present a method for directly recovering and refining the corresponding backbone coordinates in a differentiable manner.We show that interpolations in the latent space of the generator correspond to smooth deformations of the output backbones, and that test set structures not seen by the generator during training exist in its image.Finally, we perform sequence design, relaxation, and ab initio folding of a subset of generated structures, and show that in some cases we can recover the generated folds after forward-folding.Together, these results suggest a mechanism for fast protein structure refinement and folding using external energy functions.", "target": ["我们训练一个GAN来生成和恢复全原子的蛋白质骨架，并且我们表明，在选定的情况下，我们可以在序列设计和非初始正向折叠后恢复所生成的蛋白质。", "一个蛋白质骨架的生成模型，它使用了GAN、类似自动编码器的网络和细化过程，并有一组定性评价显示出正面的结果。", "本文介绍了一种使用生成式对抗网络生成蛋白质骨架的端到端方法。"]}
+{"source": "Few-Shot Learning (learning with limited labeled data) aims to overcome the limitations of traditional machine learning approaches which require thousands of labeled examples to train an effective model.Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning.The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks.Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available.In this paper, we propose a novel meta-learning paradigm wherein a few-shot learning model is learnt, which simultaneously overcomes domain shift between the train and test tasks via adversarial domain adaptation.We demonstrate the efficacy the proposed method through extensive experiments.", "target": ["元学习的Few Shot学习假设训练任务和测试任务是从同一分布中抽取的。如果不是这样，你会怎么做呢？用任务级的领域适应性进行元学习!", "本文提出了一种将无监督的对抗性领域适应与原型网络相结合的模型，该模型在有领域转移的几次学习任务上比几次学习基线表现得更好。", "作者提出了元领域适应，以解决元学习设置中的领域转移情况，在几个实验中证明了性能的改善。"]}
+{"source": "Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich and complex probabilistic models.However, this expressiveness comes at the cost of substantially complicating the process of drawing inferences from the model.In particular, inference can become challenging when the support of the model varies between executions.Though general-purpose inference engines have been designed to operate in such settings, they are typically inefficient, often relying on proposing from the prior to make transitions.To address this, we introduce a new inference framework: Divide, Conquer, and Combine (DCC).DCC divides the program into separate straight-line sub-programs, each of which has a fixed support allowing more powerful inference algorithms to be run locally, before recombining their outputs in a principled fashion.We show how DCC can be implemented as an automated and general-purpose PPS inference engine, and empirically confirm that it can provide substantial performance improvements over previous approaches.", "target": ["Divide, Conquer, and Combine是一种新的推理方案，可以在具有随机支持的概率程序上进行，即变量的存在是随机的。"]}
+{"source": "Detecting communities or the modular structure of real-life networks (e.g. a socialnetwork or a product purchase network) is an important task because the way anetwork functions is often determined by its communities.The traditional approaches to community detection involve modularity-based approaches,which generally speaking, construct partitions based on heuristics thatseek to maximize the ratio of the edges within the partitions to those betweenthem.Node embedding approaches, which represent each node in a graph as areal-valued vector, transform the problem of community detection in a graph tothat of clustering a set of vectors.Existing node embedding approaches are primarilybased on first initiating uniform random walks from each node to constructa context of a node and then seeks to make the vector representation ofthe node close to its context.However, standard node embedding approaches donot directly take into account the community structure of a network while constructingthe context around each node.To alleviate this, we explore two differentthreads of work.First, we investigate the use of biased random walks (specifically,maximum entropy based walks) to obtain more centrality preserving embeddingof nodes, which we hypothesize may lead to more effective clusters in the embeddedspace.Second, we propose a community structure aware node embeddingapproach where we incorporate modularity-based partitioning heuristics intothe objective function of node embedding.We demonstrate that our proposed approachfor community detection outperforms a number of modularity-based baselinesas well as K-means on a standard node-embedded vector space (specifically,node2vec) on a wide range of real-life networks of different sizes and densities.", "target": ["一种保存社区的节点嵌入算法，能更有效地检测出嵌入空间上有聚类的群体"]}
+{"source": "A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry.However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds.This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts.Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points.Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations.Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation.Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.", "target": ["一个用于生成多样化点云的自回归深度学习模型。", "一种将三维图形生成为点云的方法，它考虑了根据坐标对点进行的字典排序，并训练了一个模型来预测点的顺序。", "本文介绍了一��点云的生成模型，使用一个类似像素RNN的自动回归模型和一个注意力模型来处理较长距离的相互作用。"]}
+{"source": "Reinforcement learning and evolutionary algorithms can be used to create sophisticated control solutions.Unfortunately explaining how these solutions work can be difficult to due to their \"black box\" nature.In addition, the time-extended nature of control algorithms often prevent direct applications of explainability techniques used for standard supervised learning algorithms.This paper attempts to address explainability of blackbox control algorithms through six different techniques:1) Bayesian rule lists,2) Function analysis,3) Single time step integrated gradients,4) Grammar-based decision trees,5) Sensitivity analysis combined with temporal modeling with LSTMs, and6) Explanation templates.These techniques are tested on a simple 2d domain, where a simulated rover attempts to navigate through obstacles to reach a goal.For control, this rover uses an evolved multi-layer perception that maps an 8d field of obstacle and goal sensors to an action determining where it should go in the next time step.Results show that some simple insights in explaining the neural network are possible, but that good explanations are difficult.", "target": ["描述了一系列应用于导航用的简单神经网络控制器的可解释性技术。", "本文对为作为漫游车运动反控器的多层感知器提供解释的问题提出了见解和解释，以及如何解释黑盒模型的想法。"]}
+{"source": "The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal.In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress.We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components.Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set).Code is available at https://github.com/chihyaoma/selfmonitoring-agent.", "target": ["我们提出了一个用于视觉和语言导航任务的自我监控代理。", "一种视觉+语言导航的方法，该方法使用进度显示器和视觉-文本共同基础模块跟踪指令的进展，并在标准基准上表现良好。", "本文描述了一个具有全景视觉注意力和辅助性进度监测损失的视觉和语言导航模型，给出了最先进的结果。"]}
+{"source": "Environments in Reinforcement Learning (RL) are usually only partially observable.To address this problem, a possible solution is to provide the agent with information about past  observations.While common methods represent this history using a Recurrent Neural Network (RNN), in this paper we propose an alternative representation which is based on the record of the past events observed in a given episode.Inspired by the human memory, these events describe only important changes in the environment and, in our approach, are automatically discovered using self-supervision. We evaluate our history representation method using two challenging RL benchmarks: some games of the Atari-57 suite and the 3D environment Obstacle Tower.Using these benchmarks we show the advantage of our solution with respect to common RNN-based approaches.", "target": ["事件发现来代表RL中代理的历史。", "作者研究了部分观察环境下的RL问题，并提出了一个使用FFN但提供历史表示的解决方案，其性能优于PPO。", "本文提出了一种代表过去历史的新方法，作为RL代理的输入，显示出比PPO和PPO的RN变体更好的性能。"]}
+{"source": "The unconditional generation of high fidelity images is a longstanding benchmarkfor testing the performance of image decoders.Autoregressive image modelshave been able to generate small images unconditionally, but the extension ofthese methods to large images where fidelity can be more readily assessed hasremained an open problem.Among the major challenges are the capacity to encodethe vast previous context and the sheer difficulty of learning a distribution thatpreserves both global semantic coherence and exactness of detail.To address theformer challenge, we propose the Subscale Pixel Network (SPN), a conditionaldecoder architecture that generates an image as a sequence of image slices of equalsize.The SPN compactly captures image-wide spatial dependencies and requires afraction of the memory and the computation.To address the latter challenge, wepropose to use multidimensional upscaling to grow an image in both size and depthvia intermediate stages corresponding to distinct SPNs.We evaluate SPNs on theunconditional generation of CelebAHQ of size 256 and of ImageNet from size 32to 128.We achieve state-of-the-art likelihood results in multiple settings, set upnew benchmark results in previously unexplored settings and are able to generatevery high fidelity large scale samples on the basis of both datasets.", "target": ["我们表明，自回归模型可以生成高保真图像。", "一个利用解码器、尺寸放大解码器和深度放大解码器组件的架构，以解决学习图像中的长距离依赖关系的问题，从而获得高保真图像。", "本文解决了对高保真图像的生成问题，成功地展示了具有128x128分辨率的似然密度模型的令人信服的Imagenet样本。"]}
+{"source": "Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents.This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects.By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics.We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning.The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.", "target": ["一个深度分层的状态空间模型，其中相关对象的状态转换由图神经网络协调。", "当每个对象表现出显着的随机性时，多个对象的顺序动态过程的分层潜变量模型。", "本文提出了一个关系状态空间模型，模拟相关对象的联合状态转换，这些对象在一个图结构中是分层协调的。"]}
+{"source": "Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses).When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed.While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents.This paper proposes to add such inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated.Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.", "target": ["我们引入了一种新的归纳偏向，将树状结构整合到循环神经网络中。", "本文提出了ON-LSTM，一种新的RNN单元，它将潜在树结构整合到循环模型中，在语言建模、无监督解析、目标句法评价和逻辑推理上有良好的效果。"]}
+{"source": "Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures.A completely satisfactory explanation for their success remains elusive.Here, we present a novel explanation for the benefits of skip connections in training very deep networks.The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model.Several such singularities have been identified in previous works:(i) overlap singularities caused by the permutation symmetry of nodes in a given layer,(ii) elimination singularities corresponding to the elimination, i.e. consistent deactivation, of nodes,(iii) singularities generated by the linear dependence of the nodes.These singularities cause degenerate manifolds in the loss landscape that slow down learning.We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent.Moreover, for typical initializations, skip connections move the network away from the \"ghosts\" of these singularities and sculpt the landscape around them to alleviate the learning slow-down.These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.", "target": ["由模型的不可识别性产生的退化流形减缓了深度网络的学习；跳过连接通过打破退化来帮助学习。", "作者表明，消除奇点和重叠奇异点阻碍了深度神经网络的学习，并证明跳过连接可以减少这些奇异点的普遍性，加快学习速度。", "论文研究了在深度网络中使用跳过连接作为缓解训练期间Hessian矩阵的奇异性的一种方式。"]}
+{"source": "Representation learning is a central challenge across a range of machine learning areas.In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.Most prior work on representation learning has focused on generative approaches, learning representations that capture all the underlying factors of variation in the observation space in a more disentangled or well-ordered manner.In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are \"actionable\".These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, eliminating the need for explicit reconstruction.We show how these learned representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks.We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.", "target": ["学习捕捉控制所需因素的状态表征", "本文提出了一种学习表征的方法，其中欧氏距离的接近性代表了由类似政策实现的状态。", "本文提出了一种学习表征的方法，其中欧氏距离的接近性代表了由类似政策实现的状态。"]}
+{"source": "We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks.  This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter.Through simulations involving sequences of 10 related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts.We observed two key phenomena.First, forward facilitation---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, backward interference---the forgetting of the n previous tasks when learning task n+1---diminishes with n.  Forward facilitation is the goal of research on metalearning, and reduced backward interference is the goal of research on ameliorating catastrophic forgetting.We find that both of these goals are attained simply through broader exposure to a domain.", "target": ["我们研究CNN的行为，因为它在掌握新任务的同时保留了对以前学习的任务的掌握。"]}
+{"source": "We demonstrate a low effort method that unsupervisedly constructs task-optimized embeddings from existing word embeddings to gain performance on a supervised end-task.This avoids additional labeling or building more complex model architectures by instead providing specialized embeddings better fit for the end-task(s).Furthermore, the method can be used to roughly estimate whether a specific kind of end-task(s) can be learned form, or is represented in, a given unlabeled dataset, e.g. using publicly available probing tasks.We evaluate our method for diverse word embedding probing tasks and by size of embedding training corpus -- i.e. to explore its use in reduced (pretraining-resource) settings.", "target": ["Morty改进了预训练的词嵌入，以达到以下目的。 (a)提高整体嵌入性能（对于多任务设置）或提高单任务性能，同时只需要最小的努力。"]}
+{"source": "Data augmentation is commonly used to encode invariances in learning methods.However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set.The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply.In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset.We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.", "target": ["有选择地增强难以分类的点，从而实现高效的训练。", "作者研究了识别数据增殖的子采样策略问题，并提出了基于模型���响和损失的策略，以及所提方法的经验基准。", "作者提议使用影响或基于损失的方法来选择一个子集，用于增强训练模型的数据集，其中损失是对数据点的增加。"]}
+{"source": "Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules.We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules.Modeling the entire process of constructing a molecule during generation offers a number of advantages.First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions.Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes.Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product.", "target": ["一个有机分子的深度生成模型，首先生成反应物构件，然后用反应预测器组合这些构件。", "一个分子生成模型，通过两步过程生成分子，提供生成分子的合成路线，使用户能够检查生成化合物的合成可及性。"]}
+{"source": "Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks.  However, they have no inherent capability to recognize when their predictions might go wrong.There have been several efforts in the recent past to detect natural errors i.e.  misclassified inputs but these mechanisms pose additional energy requirements.  To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way.  We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs).   The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs.The combined confidence of RACs is utilized to determine if classification should terminate at an early stage.We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet.Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.", "target": ["利用隐性表征提高深度神经网络的稳定性和能源效率。", "本文旨在通过在一个或多个隐藏层之后添加基于相关特征的辅助细胞来决定是否提前结束分类，从而以节能的方式减少深度神经网络的错误分类。"]}
+{"source": "Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred.To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping.Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained.We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities.For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.", "target": ["利用词嵌入的洞察力理解知识图谱的结构。", "本文试图理解知识图谱嵌入方法的潜在结构，并证明一个模型表示关系类型的能力取决于模型架构在关系条件方面的限制。", "本文提出对链接预测（LP）模型的可解释性进行详细研究，利用最近对词嵌入的解释来对LP的模型性能进行更好的理解。"]}
+{"source": "Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements.For example, air quality monitoring system records PM2.5, CO, etc.The resulting time-series data often has missing values due to device outages or communication errors.In order to impute the missing values, state-of-the-art methods are built on Recurrent Neural Networks (RNN), which process each time stamp sequentially, prohibiting the direct modeling of the relationship between distant time stamps.Recently, the self-attention mechanism has been proposed for sequence modeling tasks such as machine translation, significantly outperforming RNN because the relationship between each two time stamps can be modeled explicitly.In this paper, we are the first to adapt the self-attention mechanism for multivariate, geo-tagged time series data.In order to jointly capture the self-attention across different dimensions (i.e. time, location and sensor measurements) while keep the size of attention maps reasonable, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.On three real-world datasets, including one our newly collected NYC-traffic dataset, extensive experiments demonstrate the superiority of our approach compared to state-of-the-art methods for both imputation and forecasting tasks.", "target": ["用于多变量、地理标记的时间序列归纳的新型自我关注机制。", "本文提出了以一种兼容有效的方式将变换器网络应用于时空数据的问题，并研究了实现三维关注的方法。", "本文实证研究了转化器模型在输入的不同维度上对时间序列数据插补的有效性。"]}
+{"source": "The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software.This work focuses on improving the quality of scanned documents in order to improve the OCR output.We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones.Deep neural network based denoising auto-encoders are trained to improve the OCR quality.We train a blind model that works on different noise levels of scanned text documents.Results are shown for blurring and watermark noise removal from noisy scanned documents.", "target": ["我们设计并测试了一个具有8个跳过连接的REDNET（ResNet Encoder-Decoder），以去除文件中的噪音，包括模糊和水印，从而形成了一个用于文件图像清理的高性能深度网络。"]}
+{"source": "The existence of adversarial examples, or intentional mis-predictions constructed from small changes to correctly predicted examples, is one of the most significant challenges in neural network research today.Ironically, many new defenses are based on a simple observation - the adversarial inputs themselves are not robust and small perturbations to the attacking input often recover the desired prediction.While the intuition is somewhat clear, a detailed understanding of this phenomenon is missing from the research literature.This paper presents a comprehensive experimental analysis of when and why perturbation defenses work and potential mechanisms that could explain their effectiveness (or ineffectiveness) in different settings.", "target": ["我们确定了一个防御技术系列，并表明确定性的有损压缩和对输入的随机扰动都导致了类似的稳定性收益。", "本文讨论了破坏特定对抗性攻击的方法，是什么让对抗性图像变得不稳定，以及攻击者是否有可能使用一个通用的扰动模型来使他们的对抗性例子对这种扰动变得稳定。", "本文研究了对抗性攻击对其输入的转变的稳定性。"]}
+{"source": "There is no consensus yet on the question whether adaptive gradient methods like Adam are easier to use than non-adaptive optimization methods like SGD.In this work, we fill in the important, yet ambiguous concept of ‘ease-of-use’ by defining an optimizer’s tunability:  How easy is it to find good hyperparameter configurations using automatic random hyperparameter search?We propose a practical and universal quantitative measure for optimizer tunability that can form the basis for a fair optimizer benchmark.  Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, we find  that Adam is the most tunable for the majority of problems, especially with a low budget for hyperparameter tuning.", "target": ["我们提供了一种对优化器进行基准测试的方法，该方法对超参数调谐过程有所认识。", "引入一个新的指标来捕捉优化器的可调性，并对不同数量的超参数调整下的深度学习优化器进行了全面的经验比较。", "本文介绍了一种简单的可调性措施，可以在资源限制下对优化器进行比较，发现调整亚当优化器的学习率最容易找到表现良好的超参数配置。"]}
+{"source": "The phase problem in diffraction physics is one of the oldest inverse problems in all of science.The central difficulty that any approach to solving this inverse problem must overcome is that half of the information, namely the phase of the diffracted beam, is always missing.In the context of electron microscopy, the phase problem is generally non-linear and solutions provided by phase-retrieval techniques are known to be poor approximations to the physics of electrons interacting with matter.Here, we show that a semi-supervised learning approach can effectively solve the phase problem in electron microscopy/scattering.In particular, we introduce a new Deep Neural Network (DNN), Y-net, which simultaneously learns a reconstruction algorithm via supervised training in addition to learning a physics-based regularization via unsupervised training.We demonstrate that this constrained, semi-supervised approach is an order of magnitude more data-efficient and accurate than the same model trained in a purely supervised fashion.In addition, the architecture of the Y-net model provides for a straightforward evaluation of the consistency of the model's prediction during inference and is generally applicable to the phase problem in other settings.", "target": ["我们引入一个半监督的深度神经网络来近似解决电子显微镜中的相位问题"]}
+{"source": "Word embeddings extract semantic features of words from large datasets of text.Most embedding methods rely on a log-bilinear model to predict the occurrenceof a word in a context of other words.Here we propose word2net, a method thatreplaces their linear parametrization with neural networks.For each term in thevocabulary, word2net posits a neural network that takes the context as input andoutputs a probability of occurrence.Further, word2net can use the hierarchicalorganization of its word networks to incorporate additional meta-data, such assyntactic features, into the embedding model.For example, we show how to shareparameters across word networks to develop an embedding model that includespart-of-speech information.We study word2net with two datasets, a collectionof Wikipedia articles and a corpus of U.S. Senate speeches.Quantitatively, wefound that word2net outperforms popular embedding methods on predicting held-out words and that sharing parameters based on part of speech further boostsperformance.Qualitatively, word2net learns interpretable semantic representationsand, compared to vector-based methods, better incorporates syntactic information.", "target": ["Word2net是一种学习单词的神经网络表征的新方法，可以利用句法信息来学习更好的语义特征。", "本文对SGNS进行了扩展，在架构上从词包模型改为前馈模型，并通过在不同的关联网络之间捆绑一个子层，贡献了一种新形式的正则化。", "一种利用上下文向量的非线性组合来学习单词的向量表示的方法，其主要想法是用神经网络取代每个单词的嵌入。"]}
+{"source": "A key goal in neuroscience is to understand brain mechanisms of cognitive functions.An emerging approach is to study “brain states” dynamics using functional magnetic resonance imaging (fMRI).So far in the literature, brain states have typically been studied using 30 seconds of fMRI data or more, and it is unclear to which extent brain states can be reliably identified from very short time series.In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used.Starting with a populational brain graph with nodes defined by a parcellation of cerebral cortex and the adjacent matrix extracted from functional connectome, GCN takes a short series of fMRI volumes as input, generates high-level domain-specific graph representations, and then predicts the corresponding cognitive state.We investigated the performance of this GCN \"cognitive state annotation\" in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution task fMRI data.Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 89% (chance level 4.8%).As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable as a base model for other transfer learning applications, for instance, adapting to new task domains.", "target": ["使用10s的fMRI信号窗口，我们的GCN模型从HCP数据集中识别了21种不同的任务条件，测试准确率为89%。"]}
+{"source": "Modern deep neural networks (DNNs) require high memory consumption and large computational loads.  In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including the line of works on factorization methods.Factorization methods approximate the weight matrix of a DNN layer with multiplication of two or multiple low-rank matrices.However, it is hard to measure the ranks of DNN layers during the training process.Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step.The former approach usually induces a high accuracy loss while the latter prevents DNN factorization from efficiently reaching a high compression rate.In this work, we propose SVD training, which first applies SVD to decompose DNN's layers and then performs training on the full-rank decomposed weights.To improve the training quality and convergence, we add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding.Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer.Singular value pruning is applied at the end to reach a low-rank model.We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.", "target": ["通过具有稀疏奇异值和正交奇异向量的SVD训练有效地诱导低等级深度神经网络。", "本文介绍了一种网络压缩的方法，鼓励每一层的权重矩阵具有低等级，并明确地将权重矩阵分解成类似SVD的因子，作为新的参数进行处理。", "建议在训练前对深度神经网络的每一层进行参数化，用低秩矩阵分解，相应地用两个连续的卷积取代卷积，然后训练分解后的方法。"]}
+{"source": "The recent rise in popularity of few-shot learning algorithms has enabled models to quickly adapt to new tasks based on only a few training samples.Previous few-shot learning works have mainly focused on classification and reinforcement learning. In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of appropriate basis functions.This enables a few labelled samples to approximate the function.We design a Feature Extractor network to encode basis functions for a task distribution, and a  Weights Generator to generate the weight vector for a novel task.We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.", "target": ["我们提出了一个专门为回归任务量身定做的少量学习模型", "本文提出了一种针对小样本回归问题的新型样本学习方法。", "一种用少量样本学习回归模型的方法，并优于其他方法。"]}
+{"source": "Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes.However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input.These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive.We propose to address this issue by discriminative detection of OOD pixels in input data.Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task.Instead, we train a dedicated OOD modelwhich discriminates the primary training set from a much larger \"background\" dataset which approximates the variety of the visual world.We perform our experiments on high resolution natural images in a dense prediction setup.We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset.We evaluate our approach on WildDash test, which is currently the only public test dataset with out-of-distribution images.The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.", "target": ["我们提出了一种在语义分割中检测异常像素的新方法。", "本文讨论了用于帮助分割过程的异常检测，并提出了一种训练二元分类器的方法，该分类器可以将已知类别集合的图像斑块与未知类别的图像斑块区分开来。", "本文旨在为语义分割检测出异常像素，这项工作利用了其他领域的数据来检测未确定的类别，以更好地模拟不确定性。"]}
+{"source": "Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices.Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead.To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs.However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large.The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results.It is difficult for even deep reinforcement learning (DRL) DDPG-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy.In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer.Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06%, and decrease the inference energy consumption by 50.69%, while achieving the same inference accuracy.", "target": ["使用分层深度强化学习进行准确、快速和自动的核素神经网络量化，具有混合精度", "一种量化神经网络权重和激活的方法，该方法使用深度强化学习来选择一个层中单个内核的位宽，并且比以前的方法取得更好的性能，或延迟。", "本文提出为神经网络中的每个内核自动搜索量化方案，使用分层RL来指导搜索。"]}
+{"source": "Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems.However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models.Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from.This poses complexity to navigate this model space to find the right model for the data and the task.In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space.Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks.Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task.The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.", "target": ["Gaggle，一个交互式视觉分析系统，帮助用户交互式地浏览模型空间，进行分类和排名任务。", "一个新的视觉分析系统，旨在使非专业用户通过使用基于示范的方法，交互式地浏览一个模型空间。", "一个视觉化分析系统，帮助新手分析师在执行分类和排名任务时浏览模型空间。"]}
+{"source": "Chinese text classification has received more and more attention today.However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media.To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN).Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder.It improves the capability of Chinese text representation.Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures.Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues.The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.", "target": ["我们提出了一种新的注意力网络和混合编码器来解决中文文本分类的文本表示问题，特别是关于发音的语言现象，如多音字和同音字。", "本文为中文文本分类任务提出了一个由单词编码器和拼音编码器组成的基于注意力的模型，并为拼音字符编码器扩展了该架构。", "提出了一个注意力网络，在这个网络中，单词和拼音都被考虑为中文表示，在几个数据集中用于文本分类，显示了改进的结果。"]}
+{"source": "Recent advances in learning from demonstrations (LfD) with deep neural networks have enabled learning complex robot skills that involve high dimensional perception such as raw image inputs. LfD algorithms generally assume learning from single task demonstrations.In practice, however, it is more efficient for a teacher to demonstrate a multitude of tasks without careful task set up, labeling, and engineering.Unfortunately in such cases, traditional imitation learning techniques fail to represent the multi-modal nature of the data, and often result in sub-optimal behavior.In this paper we present an LfD approach for learning multiple modes of behavior from visual data.Our approach is based on a stochastic deep neural network (SNN), which represents the underlying intention in the demonstration as a stochastic activation in the network.We present an efficient algorithm for training SNNs, and for learning with vision inputs, we also propose an architecture that associates the intention with a stochastic attention module.We demonstrate our method on real robot visual object reaching tasks, and show thatit can reliably learn the multiple behavior modes in the demonstration data.Video results are available at https://vimeo.com/240212286/fd401241b9.", "target": ["使用随机神经网络建模意图从非结构化演示中进行多模式模仿学习.", "一种新的基于抽样的潜变量模型推理方法，适用于多模态模仿学习，在实际的视觉机器人任务中比确定性神经网络和随机性神经网络效果更好。", "本文展示了如何利用随机神经网络从视觉数据中模仿学习几种模式，以及一种从演示中学习的方法，其中给出了同一任务的几种模式。"]}
+{"source": "The interpretability of neural networks has become crucial for their applications in real world with respect to the reliability and trustworthiness.Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation.In natural language processing, this type of explanations is challenging for human user to understand the meaning of an explanation and draw the connection between explanation and model prediction, especially for long texts.In this work, we focus on detecting the interactions between features, and propose a novel approach to build a hierarchy of explanations based on feature interactions.The proposed method is evaluated with three neural classifiers, LSTM, CNN, and BERT, on two benchmark text classification datasets.The generated explanations are assessed by both automatic evaluation measurements and human evaluators.Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models, and understandable to humans.", "target": ["通过检测特征的相互作用来构建文本分类的层次解释的新方法。", "一种为文本分类器做出的预测提供解释的新方法，在词级重要性得分上优于基线，还有一种新的指标，即内聚力损失，用于评估跨度级的重要性。", "一种基于特征相互作用和特征重要性得分的解释方法，与独立的特征贡献相比。"]}
+{"source": "Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time.FBS introduces small auxiliary connections to existing convolutional layers.In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels.FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs.We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification.Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.", "target": ["我们通过在特征计算中动态地提升和抑制通道，使卷积层运行得更快。", "一种用于动态通道修剪的特征提升和抑制方法，预测每个通道的重要性，然后使用仿生函数来放大/抑制通道的重要性。", "在测试中动态选择通道的通道修剪方法的建议。"]}
+{"source": "We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training.Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than 0.5% classification layer connection density, or less than 5% overall network connection density.We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers.Based on our sparsifying technique, we introduce the `scatter' metric to characterize the quality of a particular connection pattern.As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.", "target": ["神经网络可以被预先定义为具有稀疏的连接性，而不会出现性能下降。", "本文研究了卷积图像分类网络上层的稀疏连接模式，并介绍了在窗口/组之间分配连接的启发式方法和一种称为散点的措施，以构建连接遮蔽。", "建议通过在分类层设置稀疏的连接权重来减少深度网络学习的参数数量，并引入 \"散点 \"的概念。"]}
+{"source": "Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important problems in the development of deep learning.While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms.In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks.After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods.Based on the evaluation results, we draw several important findings and provide insights for future research.", "target": ["我们提供了一个全面的、严格的、连贯的基准来评估深度学习模型的对抗性稳定性。", "本文介绍了在各种对抗性攻击方法下对不同种类的分类模型的评估。", "一个大规模的实证研究，比较了不同的对抗性攻击和防御技术，并使用准确性与扰动预算和准确性与攻击强度曲线来评估攻击和防御。"]}
+{"source": "We propose a modification to traditional Artificial Neural Networks (ANNs), which provides the ANNs with new aptitudes motivated by biological neurons.  Biological neurons work far beyond linearly summing up synaptic inputs and then transforming the integrated information.  A biological neuron change firing modes accordingly to peripheral factors (e.g., neuromodulators) as well as intrinsic ones.  Our modification connects a new type of ANN nodes, which mimic the function of biological neuromodulators and are termed modulators, to enable other traditional ANN nodes to adjust their activation sensitivities in run-time based on their input patterns.  In this manner, we enable the slope of the activation function to be context dependent.  This modification produces statistically significant improvements in comparison with traditional ANN nodes in the context of Convolutional Neural Networks and Long Short-Term Memory networks.", "target": ["我们提出对传统的人工神经网络进行修改，以神经元的生物学为动机，使激活函数的形状取决于上下文。", "一种根据对ANN中的神经元层的输入来调整该层的激活的方法，该方法报告了高于基线的改进。", "介绍了神经网络中基本神经元的结构变化，以及在将神经元线性组合输出送入激活函数之前，用调制器将其相乘的想法。"]}
+{"source": "In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator.We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task.We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training.We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer.Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named \"mix-review''.We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated.Finally, we discuss interesting behavior of the resulting dialogue model and its implications.", "target": ["我们发现了预训练的NLG模型微调中的遗忘问题，并提出了解决该问题的mix-review策略。", "本文从语境敏感性和知识转移的角度分析了预训练-微调框架中的遗忘问题，并提出了一种优于权重衰减方法的微调策略。", "研究预训练-调整框架中的遗忘问题，特别是在对话反应生成任务中的遗忘问题，并提出了一种mix-review策略来缓解遗忘问题。"]}
+{"source": "Combining domain knowledge models with neural models has been challenging.  End-to-end trained neural models often perform better (lower Mean Square Error) than domain knowledge models or domain/neural combinations, and the combination is inefficient to train.  In this paper, we demonstrate that by composing domain models with machine learning models, by using extrapolative testing sets, and invoking decorrelation objective functions, we create models which can predict more complex systems.The models are interpretable, extrapolative, data-efficient, and capture predictable but complex non-stochastic behavior such as unmodeled degrees of freedom and systemic measurement noise.  We apply this improved modeling paradigm to several simulated systems and an actual physical system in the context of system identification.   Several ways of composing domain models with neural models are examined for time series, boosting, bagging, and auto-encoding on various systems of varying complexity and non-linearity.  Although this work is preliminary, we show that the ability to combine models is a very promising direction for neural modeling.", "target": ["复杂系统的改进建模使用了混合神经/域模型的构成、新的去相关损失函数和外插测试集", "本文通过实验比较了由物理模型、神经网络和随机模型组成的各种混合模型的外插预测，并解决了未建模动力学成为瓶颈的难题。", "本文介绍了将神经网络与非NN模型相结合的方法，以预测复杂物理系统的行为。"]}
+{"source": "Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning.In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning.Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data.Instantiating our framework results in a generalizable policy to unseen tasks.Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.", "target": ["我们从探索数据中学习密集的分数和动态模型作为先验，并利用它们在零样本条件下的新任务中诱导出一个好的政策。", "本文讨论了在新环境中的零样本泛化问题，并提出了一种在《网格世界》、《超级马里奥兄弟》和《3D机器人》上取得成果的方法。", "一种旨在为零样本泛化学习任务诊断先验的方法，其想法是在基于模型的RL框架之上采用一种建模方法。"]}
+{"source": "Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles.As a noticeable example, Stein variational gradient descent (SVGD) provides a deterministic and computationally efficient update, but it is known to underestimate the variance in high dimensions, the mechanism of which is poorly understood.In this work we explore a connection between SVGD and MMD-based inference algorithm via Stein's lemma.By comparing the two update rules, we identify the source of bias in SVGD as a combination of high variance and deterministic bias, and empirically demonstrate that the removal of either factors leads to accurate estimation of the variance.In addition, for learning high-dimensional Gaussian target, we analytically derive the converged variance for both algorithms, and confirm that only SVGD suffers from the \"curse of dimensionality\".", "target": ["分析高维度下SVGD方差塌陷的内在机制。"]}
+{"source": "We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.  The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.  Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).  Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.", "target": ["反思泛化需要重新审视旧有的想法：统计力学方法和复杂的学习行为", "作者提出，统计力学思想将有助于理解深度神经网络的泛化特性，并给出了一种方法，对有关深度神经网络和学习算法的经验结果进行了强而有力的定性描述。", "一组与理论上理解多层神经网络的泛化特性有关的想法，以及深度学习���的行为与单层和二层神经网络的定量统计物理分析结果之间的定性类比。"]}
+{"source": "Computations for the softmax function in neural network models are expensive when the number of output classes is large.This can become a significant issue in both training and inference for such models.In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.During inference, our method quickly locates the most probable expert to compute small-scale softmax.Our method is learning-based and requires no knowledge of the output class partition space a priori.We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of performance.", "target": ["我们提出了双重稀疏softmax，即稀疏专家的稀疏混合物，通过利用两级重叠的层次结构来提高softmax推理的效率。", "本文提出了一种当类的数量非常大时，对softmax计算的快速近似方法。", "本文提出了一种稀疏专家的稀疏混合物，它可以学习两级类的层次结构，以实现高效的softmax推理。"]}
+{"source": "Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.In this context, we assess the utility of observational supervision, where we take advantage of passively-collected signals such as eye tracking or “gaze” data, to reduce the amount of hand-labeled data needed for model training.Specifically, we leverage gaze information to directly supervise a visual attention layer by penalizing disagreement between the spatial regions the human labeler looked at the longest and those that most heavily influence model output.We present evidence that constraining the model in this way can reduce the number of labeled examples required to achieve a given performance level by as much as 50%, and that gaze information is most helpful on more difficult tasks.", "target": ["我们探索使用被动收集的眼球追踪数据来减少训练期间所需的标注数据量。", "一种利用凝视信息来降低模型的样本复杂度和所需的标签努力以获得目标性能的方法，在中等规模的样本和较难的任务中，结果有所改善。", "一种将凝视信号纳入标准CNN进行图像分类的方法，在模型的类激活图和由眼动追踪信息构建的图之间的差异基础上增加损失函数项。"]}
+{"source": "We study the robustness to symmetric label noise of GNNs training procedures.By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task.Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.", "target": ["我们将损失校正应用于图神经网络，以训练一个对噪声更稳定的模型。", "本文介绍了图神经网络的损失校正，以处理对称图标签噪音，重点介绍了图分类任务。", "本文提出在图神经网络的背景下使用噪音校正损失来处理噪音标签。"]}
+{"source": "Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs.Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them.We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark.Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.", "target": ["我们在一个成对的图训练系统中使用图的共同关注，用于图分类和回归。", "本文在GCN中注入了一个多头共同关注机制，允许一种药物在药物副作用预测期间关注另一种药物。", "一种用共同注意力层扩展基于图的学习的方法，该方法在成对图分类任务上优于以前的其他方法。"]}
+{"source": "In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.", "target": ["图像描述作为一种有条件的GAN训练的新颖架构，还研究了两种离散的GAN训练方法。", "一种改进的用于图像描述的GAN模型，提出了一种上下文感知的LSTM标题机，引入了性能更强的共同注意判别器，并使用SCST进行GAN训练。"]}
+{"source": "We present Newtonian Monte Carlo (NMC), a method to improve Markov Chain Monte Carlo (MCMC) convergence by analyzing the first and second order gradients of the target density to determine a suitable proposal density at each point.Existing first order gradient-based methods suffer from the problem of determining an appropriate step size.Too small a step size and it will take a large number of steps to converge, while a very large step size will cause it to overshoot the high density region.NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension.However, our objective is not to find a maxima but instead to find a parameterized density that can best match the local curvature of the target density.  This parameterized density is then used as a single-site Metropolis-Hastings proposal.As a further improvement on first order methods, we show that random variables with constrained supports don't need to be transformed before taking a gradient step.NMC directly matches constrained random variables to a proposal density with the same support thus keeping the curvature of the target density intact.We demonstrate the efficiency of NMC on a number of different domains.For statistical models where the prior is conjugate to the likelihood, our method recovers the posterior quite trivially in one step.However, we also show results on fairly large non-conjugate models, where NMC performs better than adaptive first order methods such as NUTS or other inexact scalable inference methods such as Stochastic Variational Inference or bootstrapping.", "target": ["利用曲率使MCMC方法的收敛速度快于现有技术水平。"]}
+{"source": "Neural Tangents is a library designed to enable research into infinite-width neural networks.It provides a high-level API for specifying complex and hierarchical neural network architectures.These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit.Infinite-width networks can be trained analytically using exact Bayesian inference or using gradient descent via the Neural Tangent Kernel.Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks in either function space or weight space. The entire library runs out-of-the-box on CPU, GPU, or TPU.All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. Neural Tangents is availableathttps://www.github.com/google/neural-tangentsWe also provide an accompanying interactive Colab notebook athttps://colab.sandbox.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb", "target": ["Keras适用于无限的神经网络。"]}
+{"source": "Deep neural networks have achieved great success in classification tasks during the last years.However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage.In this work, we propose a methodology for training a neural network that allows it to efficiently detect out-of-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes.Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classification tasks.Additionally, the way this method was constructed makes it suitable for training any classification algorithm that is based on Maximum Likelihood methods.", "target": ["我们提出了一个新的损失函数，在图像和文本分类任务中，通过Outlier Exposure实现了最先进的异常检测结果。", "本文通过调整Outlier Exposure技术的损失函数来解决异常检测和模型校准的问题，结果表明在视觉和文本基准上的性能比OE有所提高，并改进了模型校准。", "提出了一个新的损失函数，用于训练具有Outlier Exposure的网络，与使用KL发散的简单损失函数相比，它产生了更好的OOD检测。"]}
+{"source": "Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map.The precise form of this representation is often considered to be a metric representation of space.An internal representation, however, is judged by its contribution to performance on a given task, and may thus vary between different types of navigation tasks.Here we train a recurrent neural network that controls an agent performing several navigation tasks in a simple environment.To focus on internal representations, we split learning into a task-agnostic pre-training stage that modifies internal connectivity and a task-specific Q learning stage that controls the network's output.We show that pre-training shapes the attractor landscape of the networks, leading to either a continuous attractor, discrete attractors or a disordered state.These structures induce bias onto the Q-Learning phase, leading to a performance pattern across the tasks corresponding to metric and topological regularities.Our results show that, in recurrent networks, inductive bias takes the form of attractor landscapes -- which can be shaped by pre-training and analyzed using dynamical systems methods.Furthermore, we demonstrate that non-metric representations are useful for navigation tasks.", "target": ["任务无关的预训练可以塑造RNN的吸引子景观，并为不同的导航任务形成不同的感应偏向", "本文研究了在导航任务上训练的循环神经网络的内部表征，发现预先训练使用路径整合的RNN包含二维连续吸引子，而预先训练地标记忆的RNN包含离散吸引子。", "本文探讨了在不同的导航目标上对循环网络进行预训练如何为解决下游任务带来不同的好处，并展示了不同的预训练如何在预训练后表现为网络中不同的动态结构。"]}
+{"source": "Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features.In this context, it has also been observed that folding the verification procedure into training makes it easier to train verifiably robust models.In this paper, we extend the applicability of verified training by extending it to (1) recurrent neural network architectures and (2) complex specifications that go beyond simple adversarial robustness, particularly specifications that capture temporal properties like requiring that a robot periodically visits a charging station or that a language model always produces sentences of bounded length.Experiments show that while models trained using standard training often violate desired specifications, our verified training method produces models that both perform well (in terms of test error or reward) and can be shown to be provably consistent with specifications.", "target": ["时间属性和序列生成模型的神经网络验证", "本文将区间约束传播扩展到循环计算和自动回归模型，介绍并扩展了用于指定时间约束的信号时态逻辑，并提供了带有约束传播的STL可以确保神经模型符合时间规范的证明。", "根据信号时态逻辑定义的一组规则，可核查地训练时间序列回归器的方法，以及在推导STL语言的约束传播规则方面的方法。"]}
+{"source": "Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains.Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency.Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data.Nevertheless, there are many applications with all kinds of tabular data in other domains.Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains.To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically.Specifically, the design of TabNN follows two principles: \\emph{to explicitly leverages expressive feature combinations} and \\emph{to reduce model complexity}.Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN.Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.", "target": ["我们提出了一个通用的神经网络解决方案，为表格数据自动得出有效的NN架构。", "一种为表格数据而设计的新的神经网络训练程序，旨在利用从GBDTs中提取的特征集群。", "提议使用梯度提升决策树和深度神经网络的混合机器学习算法，打算在表格数据上进行研究方向。"]}
+{"source": "Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic.These KBs are typically used to perform query inferences and rule mining.But their efficacy is only as high as their completeness.Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs.Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way.For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns.But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules.It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB.In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago.Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.", "target": ["使用提升推理的概率规则学习系统", "一个概率规则学习的模型，用于自动完成概率数据库，该模型使用AMIE+和提升推理来帮助计算效率。"]}
+{"source": "Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself.These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values.However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \\textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns.In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots.In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level.Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.", "target": ["我们提出了第一个用于对话状态跟踪（DST）的非自回归神经模型，在MultiWOZ2.1基准上达到了SOTA的准确性（49.04%），并将推理延迟降低了一个数量级。", "一种用于DST任务的新模型，通过非自回归解码器降低推理时间的复杂性，获得有竞争力的DST精度，并显示出比其他基线的改进。", "提议建立一个能够以非循环方式追踪对话状态的模型。"]}
+{"source": "The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane.In contrast, the optical zoom changes the focal length and the digital zoom is used to enlarge a certain region of an image to the original image size.In this paper, we are the first to formulate an unsupervised 3D-zoom learning problem where images with an arbitrary zoom factor can be generated from a given single image.An unsupervised framework is convenient, as it is a challenging task to obtain a 3D-zoom dataset of natural scenes due to the need for special equipment to ensure camera movement is restricted to the Z-axis.Besides, the objects in the scenes should not move when being captured, which hinders the construction of a large dataset of outdoor scenes.We present a novel unsupervised framework to learn how to generate arbitrarily 3D-zoomed versions of a single image, not requiring a 3D-zoom ground truth, called the Deep 3D-Zoom Net.The Deep 3D-Zoom Net incorporates the following features:(i) transfer learning from a pre-trained disparity estimation network via a back re-projection reconstruction loss;(ii) a fully convolutional network architecture that models depth-image-based rendering (DIBR), taking into account high-frequency details without the need for estimating the intermediate disparity; and(iii) incorporating a discriminator network that acts as a no-reference penalty for unnaturally rendered areas.Even though there is no baseline to fairly compare our results, our method outperforms previous novel view synthesis research in terms of realistic appearance on large camera baselines.We performed extensive experiments to verify the effectiveness of our method on the KITTI and Cityscapes datasets.", "target": ["一种新颖的网络架构，用于执行深度3D缩放或特写。", "一种为给定的输入图像创建 \"放大图像 \"的方法，以及一种新颖的反投影重建损失，使网络能够学习潜在的三维结构并保持自然外观。", "当摄像机向前移动时，合成三维变焦行为的算法，在GANs框架中纳入差异估计的网络结构，以合成新的视图，以及提议的新电脑视觉任务。"]}
+{"source": "The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions σ, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function f to any given approximation threshold ε, if and only if σ is non-polynomial.In this paper, we give a direct algebraic proof of the theorem.Furthermore we shall explicitly quantify the number of hidden units required for approximation.Specifically, if X in R^n is compact, then a neural network with n input units, m output units, and a single hidden layer with {n+d choose d} hidden units (independent of m and ε), can uniformly approximate any polynomial function f:X -> R^m whose total degree is at most d for each of its m coordinate functions.In the general case that f is any continuous function, we show there exists some N in O(ε^{-n}) (independent of m), such that N hidden units would suffice to approximate f.We also show that this uniform approximation property (UAP) still holds even under seemingly strong conditions imposed on the weights.We highlight several consequences:(i) For any δ > 0, the UAP still holds if we restrict all non-bias weights w in the last layer to satisfy |w| < δ.(ii) There exists some λ>0 (depending only on f and σ), such that the UAP still holds if we restrict all non-bias weights w in the first layer to satisfy |w|>λ.(iii) If the non-bias weights in the first layer are *fixed* and randomly chosen from a suitable range, then the UAP holds with probability 1.", "target": ["通过代数方法对普遍近似定理进行量化改良。", "作者以代数方式推导出通用近似属性证明，并断言这些结果通用于其他种类的神经网络和类似的学习者。", "Leshno版本的神经网络通用近似属性的新证明，以及对通用近似属性的新见解。"]}
+{"source": "In this paper, we design a generic framework for learning a robust text classification model that achieves accuracy comparable to standard full models under test-timebudget constraints.We take a different approach from existing methods and learn to dynamically delete a large fraction of unimportant words by a low-complexity selector such that the high-complexity classifier only needs to process a small fraction of important words.In addition, we propose a new data aggregation method to train the classifier, allowing it to make accurate predictions even on fragmented sequence of words.Our end-to-end method achieves state-of-the-art performance while its computational complexity scales linearly with the small fraction of important words in the whole corpus.Besides, a single deep neural network classifier trained by our framework can be dynamically tuned to different budget levels at inference time.", "target": ["模块化的文档分类框架和数据聚合技术，使框架对各种失真和噪音具有稳定性，并只关注重要的词。", "作者考虑在测试时间预测存在资源限制的情况下，训练基于RNN的文本分类，并提供了一种使用掩蔽机制的方法，以减少预测中使用的单词/短语/句子，然后用分类器来处理这些成分。"]}
+{"source": "Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-net and searching for an optimal architecture.In this paper, we present a novel approach, namely  Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance.In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut.This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels.We solve it by introducing  edge normalization, which adds a new set of edge-level hyper-parameters to reduce uncertainty in search.Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoy both faster speed and higher training stability.Experiment results demonstrate the effectiveness of the proposed method.Specifically, we achieve an error rate of 2.57% on CIFAR10 within merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) within 3.8 GPU-days for search.Our code has been made available at https://www.dropbox.com/sh/on9lg3rpx1r6dkf/AABG5mt0sMHjnEJyoRnLEYW4a?dl=0.", "target": ["允许超级网络中的部分通道连接，以规范和加速可微分的架构搜索", "神经结构搜索方法DARTS的扩展，通过使用通道的随机子集和规范化边缘的方法来解决其巨大的内存成本的缺点。", "本文从大的内存和计算开销出发，提出在训练效率方面改进DARTS，并提出了一种部分通道连接和边缘正规化的DARTS。"]}
+{"source": "Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a more straightforward learning signal.Humans effortlessly combine the two, and engage in chit-chat for example with the goal of exchanging information or eliciting a specific response.Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue.Specifically, we train a goal-oriented model with reinforcement learning via self-play against an imitation-learned chit-chat model with two new approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-k utterances.We show that both models outperform a strong inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.", "target": ["代理人进行互动（说话、行动），并能在一个具有多样化语言的丰富世界中实现目标，连接了闲聊和目标导向的对话之间的差距。", "本文研究了一个多代理的对话任务，在这个任务中，学习代理的目的是产生自然语言行动，以引起另一个代理的特定行动，并显示RL-代理可以达到比模仿学习基线更高的任务完成水平。", "本文探讨了以目标为导向的对话设置与强化学习在一个科幻文本冒险游戏中的应用，并观察到RL方法优于监督学习模型。"]}
+{"source": "We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies.Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation.We propose estimated mixture policy (EMP), a novel class of partially policy-agnostic methods to accurately estimate those quantities.With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction.In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods.", "target": ["一种新的部分政策不可知的方法，用于具有多个已知或未知行为政策的无限期非政策政策评估。", "一种估计的混合政策，它采用了非政策性政策评价的无限期估计器和重要性权重的回归重要性抽样的思想，并将其扩展到许多政策和未知政策。", "一种通过估计回归下的混合政策来解决具有多种行为政策的无限期非政策评价的算法，以及估计政策比率可以减少方差的理论证明。"]}
+{"source": "We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of structure.Our gradient flow derives its sparsity pattern from the minimally faithful inverse of its underlying graphical model.We find that this factorization reduces the necessary numbers both of parameters in the neural network and of adaptive integration steps in the ODE solver.Consequently, the throughput at training time and inference time is increased, without decreasing performance in comparison to unconstrained flows.By expressing the structural inversion and the flow construction as compilation passes of a probabilistic programming language, we demonstrate their applicability to the stochastic inversion of realistic models such as convolutional neural networks (CNN).", "target": ["我们为摊销推理引入了一个更有效的神经架构，它利用原则性的稀疏结构选择将连续和条件标准化流结合起来。"]}
+{"source": "We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings.For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as 17 weight parameters, providing >90 % compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward.We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources.", "target": ["我们表明，在RL中具有ES优化的ENAS是高度可扩展的，并利用它通过权重共享来压缩神经网络策略。", "作者通过压缩前馈神经网络，迫使其共享权重，并使用强化学习方法学习共享权重的映射，构建了极少参数的强化学习策略。", "本文结合了ENAS和ES方法的优化想法，并引入了彩色网络架构，将RL网络的权重划分为绑定的子组。"]}
+{"source": "Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.Typically anomaly detection is treated as an unsupervised learning problem.In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous.Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples.Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific.In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection.Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution.We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.", "target": ["我们介绍了Deep SAD，这是一种用于一般半监督性异常检测的深度方法，特别是利用了标记的异常情况。", "一种新的方法来寻找异常数据，当给定一些标记的异常数据时，应用信息理论衍生的损失，基于正常数据通常比异常数据的熵低。", "提出了在无标签数据、有标签的阳性数据和有标签的阴性数据的情况下的异常检测框架，并建议从信息论的角度来处理半监督的AD。"]}
+{"source": "To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD).Our contributions are two-fold.First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called  \\emph{interpolation setting}, there exists many-to-one \\emph{alignment} between student and teacher nodes in the lowest layer under mild conditions.This suggests that generalization in unseen dataset is achievable, even the same condition often leads to zero training error.Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are learned first and subtle teacher nodes are left unlearned until late stage of training.As a result, it could take a long time to converge into these small-gradient critical points.Our analysis shows that over-parameterization plays two roles: (1) it is a necessary condition for alignment to happen at the critical points, and (2) in training dynamics, it helps student nodes cover more teacher nodes with fewer iterations.Both improve generalization.Experiments justify our finding.", "target": ["本文分析了在师生环境下通过SGD训练深度ReLU网络的训练动态和关键点。", "研究学生-教师多层ReLU网络中的过度参数化，理论部分是关于教师-学生设置的SGD临界点，启发式和经验式部分是关于SDG算法作为教师网络的函数的动态性。"]}
+{"source": "We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training $L$-hidden-layer linear residual networks (ResNets).We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss.Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets.Compared with the global convergence result of GD for training standard deep linear networks \\citep{du2019width}, our condition on the neural network width is sharper by a factor of $O(\\kappa L)$, where $\\kappa$ denotes the condition number of the covariance matrix of the training data.In addition, for the first time we establish the global convergence of SGD for training deep linear ResNets and prove a linear convergence rate when the global minimum is $0$.", "target": ["在输入和输出线性变换的某些条件下，GD和SGD都可以实现全域收敛，用于训练深度线性ResNets。", "作者研究了梯度下降在训练深度线性残差网络中的收敛性，并建立了GD/SGD的全域收敛和SG/SGD的线性收敛率。", "研究GD和SGD在深层线性网络上的收敛特性，并证明在输入和输出变换的某些条件下，在初始化为零的情况下，GD和SGD会收敛到全域最小值。"]}
+{"source": "In this paper, we empirically investigate the training journey of deep neural networks relative to fully trained shallow machine learning models.We observe that the deep neural networks (DNNs) train by learning to correctly classify shallow-learnable examples in the early epochs before learning the harder examples.We build on this observation this to suggest a way for partitioning the dataset into hard and easy subsets that can be used for improving the overall training process.Incidentally, we also found evidence of a subset of intriguing examples across all the datasets we considered, that were shallow learnable but not deep-learnable.In order to aid reproducibility, we also duly release our code for this work at https://github.com/karttikeya/Shallow_to_Deep/", "target": ["我们分析了深度网络的训练过程，并表明它们从快速学习浅层可分类的例子开始，慢慢地归纳到更难的数据点。"]}
+{"source": "While much recent work has targeted learning deep discrete latent variable models with variational inference, this setting remains challenging, and it is often necessary to make use of potentially high-variance gradient estimators in optimizing the ELBO.As an alternative, we propose to optimize a non-ELBO objective derived from the Bethe free energy approximation to an MRF's partition function.This objective gives rise to a saddle-point learning problem, which we train inference networks to approximately optimize.The derived objective requires no sampling, and can be efficiently computed for many MRFs of interest.We evaluate the proposed approach in learning high-order neural HMMs on text, and find that it often outperforms other approximate inference schemes in terms of true held-out log likelihood.At the same time, we find that all the approximate inference-based approaches to learning high-order neural HMMs we consider underperform learning with exact inference by a significant margin.", "target": ["用来自Bethe分区函数近似的鞍点目标学习深层潜变量MRFs。", "一种学习深度潜伏变量MRF的方法，其优化目标是利用Bethe自由能，同时也解决了Bethe自由能优化的基本限制。", "基于Bethe自由能和摊销推理的潜变量MRF学习目标，与优化标准ELBO不同。"]}
+{"source": "In an explanation generation problem, an agent needs to identify and explain the reasons for its decisions to another agent.Existing work in this area is mostly confined to planning-based systems that use automated planning approaches to solve the problem.In this paper, we approach this problem from a new perspective, where we propose a general logic-based framework for explanation generation.In particular, given a knowledge base $KB_1$ that entails a formula $\\phi$ and a second knowledge base $KB_2$ that does not entail $\\phi$, we seek to identify an explanation $\\epsilon$ that is a subset of $KB_1$ such that the union of $KB_2$ and $\\epsilon$ entails $\\phi$.We define two types of explanations, model- and proof-theoretic explanations, and use cost functions to reflect preferences between explanations.Further, we present our algorithm implemented for propositional logic that compute such explanations and empirically evaluate it in random knowledge bases and a planning domain.", "target": ["一个使用逻辑学生成解释的一般框架。", "本文从KR的角度研究了解释的生成，并进行了测量随机公式和Blocksworld实例中的公式的解释大小和运行时间的实验。", "本文提供了一个关于两个知识库之间解释的视角，并���规划文献中关于模型调和的工作并行。"]}
+{"source": "Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the vanishing gradient problem.This problem can be typically resolved by the rectified linear unit (ReLU) activation.However, here we show that even for such activation, deep and narrow neural networks (NNs) will converge to erroneous mean or median states of the target function depending on the loss with high probability.Deep and narrow NNs are encountered in solving partial differential equations with high-order derivatives.We demonstrate this collapse of such NNs both numerically and theoretically, and provide estimates of the probability of collapse.We also construct a diagram of a safe region for designing NNs that avoid the collapse to erroneous states.Finally, we examine different ways of initialization and normalization that may avoid the collapse problem.Asymmetric initializations may reduce the probability of collapse but do not totally eliminate it.", "target": ["深度和狭义的神经网络将根据损失的情况，高概率地收敛到目标函数的错误的平均值或中位数状态。", "本文研究了深而窄的网络的失败模式，重点是尽可能用小的模型，对于这些模型来说，不希望发生的行为。", "本文表明，如果隐层宽度太小，深度ReLU神经网络的训练将以高概率收敛到一个恒定的分类器，而不是随机初始化。"]}
+{"source": "We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the \"shortest successful perturbation\", demonstrating a close connection between adversarial losses and the margins.We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. Instead of adversarial training with a fixed $\\epsilon$, MMA offers an improvement by enabling adaptive selection of the \"correct\" $\\epsilon$ as the margin individually for each datapoint.In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins.Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\\ell_\\infty$ and $\\ell_2$ robustness.", "target": ["我们提出MMA训练来直接最大化输入空间余量，以便主要通过消除特定固定失真界限的要求来提高对抗性的稳定性。", "通过最大化输入到决策边界的最短边际，一种基于自适应边际的对抗性训练方法来训练稳定的DNN，这使得大扰动下的对抗性训练成为可能。", "介绍了一种针对对抗性攻击的稳定学习方法，其中输入空间余量被直接最大化，并介绍了最大余量的软性变体。"]}
+{"source": "Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks.Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous.  We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.", "target": ["我们提出了一种用GANs进行异常检测的方法，即通过搜索生成器的潜在空间来寻找好的样本表示。", "作者提出使用GAN进行异常检测，采用基于梯度下降法来迭代更新潜在表征，并对生成器进行了新的参数更新。", "一种基于GAN的方法来进行图像数据的异常检测，其中生成器的潜在空间被探索，以找到测试图像的代表。"]}
+{"source": "Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased.In this paper, we analyze gradient-based MCMC and VI procedures and find theoretical and empirical evidence that these procedures are not as different as one might think.In particular, a close examination of the Fokker-Planck equation that governs the Langevin dynamics (LD) MCMC procedure reveals that LD implicitly follows a gradient flow that corresponds to a variational inference procedure based on optimizing a nonparametric normalizing flow.This result suggests that the transient bias of LD (due to too few warmup steps) may track that of VI (due to too few optimization steps), up to differences due to VI’s parameterization and asymptotic bias.Empirically, we find that the transient biases of these algorithms (and momentum-accelerated versions) do evolve similarly.This suggests that practitioners with a limited time budget may get more accurate results by running an MCMC procedure (even if it’s far from burned in) than a VI procedure, as long as the variance of the MCMC estimator can be dealt with (e.g., by running many parallel chains).", "target": ["基于梯度的MCMC和变异推理算法的短暂行为比人们想象的更相似，这让人们对变异推理比MCMC更快的说法产生怀疑。"]}
+{"source": "Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data.However, the primary focus has been on handling simple undirected graphs.Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only.In this paper, we propose CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph.CompGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations.It also generalizes several of the existing multi-relational GCN methods.We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results.We make the source code of CompGCN available to foster reproducible research.", "target": ["用于多关系图的基于组合的图卷积框架。", "作者在多关系图上开发了GCN，并提出了CompGCN，它利用知识图嵌入的洞察力，学习节点和关系表示，以缓解过度参数化的问题。", "本文介绍了一个多关系图的GCN框架，并将现有的几种知识图谱嵌入方法概括为一个框架。"]}
+{"source": "State-of-the-art neural machine translation methods employ massive amounts of parameters.Drastically reducing computational costs of such methods without affecting performance has been up to this point unsolved.In this work, we propose a quantization strategy tailored to the Transformer architecture.We evaluate our method on the WMT14 EN-FR and WMT14 EN-DE translation tasks and achieve state-of-the-art quantization results for the Transformer, obtaining no loss in BLEU scores compared to the non-quantized baseline.We further compress the Transformer by showing that, once the model is trained, a good portion of the nodes in the encoder can be removed without causing any loss in BLEU.", "target": ["我们将变形器完全量化为8位，与全精度模型相比，提高了翻译质量。", "一种对机器翻译模型Transformer进行量化的8位元量化方法，提出在推理过程中使用统一的最小-最大量化，并在量化前进行桶状权重以减少量化误差。", "一种通过量化技术减少所需内存空间的方法，重点是为Transformer架构减少。"]}
+{"source": "Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems.However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes.We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space.The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters.Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks.Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.", "target": ["Latent Embedding Optimization（LEO）是一种新型的基于梯度的元学习器，在具有挑战性的5-way 1-shot和5-shot miniImageNet和tieredImageNet分类任务上具有最先进的性能。", "一个新的元学习框架，学习与数据相关的潜空间，在潜空间中快速适应，对少样本学习有效，对适应有任务依赖的初始化，对多模态任务分布效果好。", "本文提出了一种用于元学习的潜在嵌入优化方法，并声称其贡献在于将基于优化的元学习技术与模型参数的高维空间脱离。"]}
+{"source": "We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability.Our architecture encodes an image as a set of vectors, and applies an iterative message-passing procedure to discover and reason about relevant entities and relations in a scene.In six of seven StarCraft II Learning Environment mini-games, our agent achieved state-of-the-art performance, and surpassed human grandmaster-level on four.In a novel navigation and planning task, our agent's performance and learning efficiency far exceeded non-relational baselines, it was able to generalize to more complex scenes than it had experienced during training.Moreover, when we examined its learned internal representations, they reflected important structure about the problem and the agent's intentions.The main contribution of this work is to introduce techniques for representing and reasoning about states in model-free deep reinforcement learning agents via relational inductive biases.Our experiments show this approach can offer advantages in efficiency, generalization, and interpretability, and can scale up to meet some of the most challenging test environments in modern artificial intelligence.", "target": ["关系性归纳偏见提高了无模型强化学习代理中的异常概括能力", "一个共享的关系网络架构，用于参数化行为者和批评者网络，专注于分布式优势行为者-批评者算法，用关于环境的关系知识增强无模型的深度强化技术，以便行为者能够学习可解释的状态表示。", "在无模型RL的背景下，对与关系网络相结合的自我注意机制进行了定量和定性的分析和评估。"]}
+{"source": "Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain.It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training.When paired training data is not accessible, image translation becomes an ill-posed problem.We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term.We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input.Thus we propose to add an attention module to predict an attention map to guide the image translation process.The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes.The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection.Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.", "target": ["我们提出了一个简单的生成模型，用于无监督的图像翻译和显着性检测。"]}
+{"source": "Building deep neural networks to control autonomous agents which have to interact in real-time with the physical world, such as robots or automotive vehicles, requires a seamless integration of time into a network’s architecture.The central question of this work is, how the temporal nature of reality should be reflected in the execution of a deep neural network and its components.Most artificial deep neural networks are partitioned into a directed graph of connected modules or layers and the layers themselves consist of elemental building blocks, such as single units.For most deep neural networks, all units of a layer are processed synchronously and in parallel, but layers themselves are processed in a sequential manner.In contrast, all elements of a biological neural network are processed in parallel.In this paper, we define a class of networks between these two extreme cases.These networks are executed in a streaming or synchronous layerwise-parallel manner, unlocking the layers of such networks for parallel processing.Compared to the standard layerwise-sequential deep networks, these new layerwise-parallel networks show a fundamentally different temporal behavior and flow of information, especially for networks with skip or recurrent connections.We argue that layerwise-parallel deep networks are better suited for future challenges of deep neural network design, such as large functional modularized and/or recurrent architectures as well as networks allocating different network capacities dependent on current stimulus and/or task complexity.We layout basic properties and discuss major challenges for layerwise-parallel networks.Additionally, we provide a toolbox to design, train, evaluate, and online-interact with layerwise-parallel networks.", "target": ["我们定义了一个分层模型并行的深度神经网络的概念，对于这些网络来说，各层是平行运行的，并提供了一个工具箱来设��、训练、评估和与这些网络在线互动。", "一个用Theano编写的用于并行神经元更新的GPU加速工具箱，支持循环网络和具有跳层连接的网络中的不同更新顺序。", "深度神经网络学习和评估的新工具箱，以及从层序网络到层序并行网络的规范转换建议。"]}
+{"source": "Deep neural networks are known to be vulnerable to adversarial perturbations.In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems.From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets.This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust.The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently.Experiments show that our method effectively improves deep model's adversarial robustness.", "target": ["衔接深度神经网络稳定性与Lyapunov稳定性的对抗性防御方法", "作者将训练NN表述为寻找离散动态系统的最优控制器，使他们能够使用连续逼近法来训练NN，以使其对对抗性攻击更加强大。", "本文使用神经网络作为离散化的ODE的理论观点，发展了一个旨在训练网络同时执行稳定性的稳定控制理论。"]}
+{"source": "In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.We prove that DrGCNs can reduce the variance of the node representations by connecting our problem to the theory of the mean field.However, practically, we find that the degrees DrGCNs help vary severely on different datasets.We revisit the problem and develop a new measure K to quantify the effect.This measure guides when we should use dimensional reweighting in GCNs and how much it can help.Moreover, it offers insights to explain the improvement obtained by the proposed DrGCNs.The dimensional reweighting block is light-weighted and highly flexible to be built on most of the GCN variants.Carefully designed experiments, including several fixes on duplicates, information leaks, and wrong labels of the well-known node classification benchmark datasets, demonstrate the superior performances of DrGCNs over the existing state-of-the-art approaches.Significant improvements can also be observed on a large scale industrial dataset.", "target": ["我们提出了一个简单而有效的GCN的重新加权方案，在理论上得到了平均场理论的支持。", "一种被称为DrGCN的方法，通过减少维度之间的变异，对图卷积网络中节点表示的不同维度进行重新加权。"]}
+{"source": "Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge.As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter.The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge.Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation.We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks.We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018).", "target": ["我们的方法是首次尝试利用顺序潜变量模型在多轮知识基础的对话中进行知识选择。它在Wizard of Wikipedia基准上实现了新的最先进的性能。", "一种用于对话生成中的知识选择的顺序潜变量模型，它将后验注意模型扩展到潜知识选择问题上，并取得了比以前最先进的模型更好的性能。", "一种选择以知识为基础的多轮对话的新型架构，在相关基准数据集上产生了最先进的技术，并在人类评价中获得了更高的分数。"]}
+{"source": "Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data.State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly.These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking.We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior.We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks.", "target": ["我们提出了一种元学习方法，该方法在不同的训练事件中有效地摊销了层次化的变异推理。", "对MAML型模型的改编，通过在MAML的分层贝叶斯观点中采用任务特定参数的变异推理，说明了任务特定潜变量的后验不确定性。", "作者考虑通过元学习来学习神经网络权重的先验，通过摊销的变异推理来完成。"]}
+{"source": "Often we wish to transfer representational knowledge from one neural network to another.Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator.Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network.We demonstrate that this objective ignores important structural knowledge of the teacher network.This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data.We formulate this objective as contrastive learning.Experiments demonstrate that our resulting new objective outperforms knowledge distillation on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer.When combined with knowledge distillation, our method sets a state of the art in many transfer tasks, sometimes even outperforming the teacher network.", "target": ["通过最大限度地增加教师和学生之间的相互信息来进行表述/知识蒸馏", "本文结合了衡量教师和学生网络所学到的表征之间的相互信息的对比性目标来进行模型蒸馏，并提出了一个在蒸馏任务上比现有替代方案有改进的模型。"]}
+{"source": "Developing effective biologically plausible learning rules for deep neural networks is important for advancing connections between deep learning and neuroscience.To date, local synaptic learning rules like those employed by the brain have failed to match the performance of backpropagation in deep networks.In this work, we employ meta-learning to discover networks that learn using feedback connections and local, biologically motivated learning rules.Importantly, the feedback connections are not tied to the feedforward weights, avoiding any biologically implausible weight transport.It can be shown mathematically that this approach has sufficient expressivity to approximate any online learning algorithm.Our experiments show that the meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures.Moreover, we demonstrate empirically that this model outperforms a state-of-the-art gradient-based meta-learning algorithm for continual learning on regression and classification benchmarks.This approach represents a step toward biologically plausible learning mechanisms that can not only match gradient descent-based learning, but also overcome its limitations.", "target": ["利用反馈连接和区域可塑性规则进行学习的网络，可以利用元学习进行优化。"]}
+{"source": "In the visual system, neurons respond to a patch of the input known as their classical receptive field (RF), and can be modulated by stimuli in the surround.These interactions are often mediated by lateral connections, giving rise to extra-classical RFs.We use supervised learning via backpropagation to learn feedforward connections, combined with an unsupervised learning rule to learn lateral connections between units within a convolutional neural network.These connections allow each unit to integrate information from its surround, generating extra-classical receptive fields for the units in our new proposed model (CNNEx).We demonstrate that these connections make the network more robust and achieve better performance on noisy versions of the MNIST and CIFAR-10 datasets.Although the image statistics of MNIST and CIFAR-10 differ greatly, the same unsupervised learning rule generalized to both datasets.Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding the computations implemented by feedforward connections when the input is unreliable.", "target": ["以非监督方式学习的具有生物启发的横向连接的CNN对嘈杂的输入更加坚固。"]}
+{"source": "Deep learning (DL) has in recent years been widely used in naturallanguage processing (NLP) applications due to its superiorperformance.However, while natural languages are rich ingrammatical structure, DL has not been able to explicitlyrepresent and enforce such structures.This paper proposes a newarchitecture to bridge this gap by exploiting tensor productrepresentations (TPR), a structured neural-symbolic frameworkdeveloped in cognitive science over the past 20 years, with theaim of integrating DL with explicit language structures and rules.We call it the Tensor Product Generation Network(TPGN), and apply it to image captioning.The keyideas of TPGN are:1) unsupervised learning ofrole-unbinding vectors of words via a TPR-based deep neuralnetwork, and2) integration of TPR with typical DL architecturesincluding Long Short-Term Memory (LSTM) models.The novelty of ourapproach lies in its ability to generate a sentence and extractpartial grammatical structure of the sentence by usingrole-unbinding vectors, which are obtained in an unsupervisedmanner.Experimental results demonstrate the effectiveness of theproposed approach.", "target": ["本文旨在为基于深度学习的自然语言处理应用开发一种张量积表示方法。"]}
+{"source": "It is well-known that  classifiers are vulnerable to adversarial perturbations.To defend against adversarial perturbations, various certified robustness results have been derived.However, existing certified robustnesses are limited to top-1 predictions.In many real-world applications, top-$k$ predictions are more relevant.In this work, we aim to derive certified robustness for top-$k$ predictions.In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example.We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier.We derive a tight robustness in $\\ell_2$ norm for top-$k$ predictions  when using randomized smoothing with Gaussian noise.We find that generalizing the certified robustness  from top-1 to top-$k$ predictions faces significant technical challenges.We also empirically evaluate our method on CIFAR10 and ImageNet.For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\\% when the $\\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255).Our code is publicly available at: \\url{https://github.com/jjy1994/Certify_Topk}.", "target": ["我们研究了在高斯噪声下通过随机平滑对top-k预测的认证稳定性，并得出了L_2规范下的严格稳定性约束。", "本文扩展了使用随机平滑法推导认证半径的工作，并展示了高斯扰动下的平滑分类器对前k个预测的认证半径。", "本文在top-1预测的随机平滑技术的基础上，旨在提供top-k预测的认证。"]}
+{"source": "Recent work has shown increased interest in using the Variational Autoencoder (VAE) framework to discover interpretable representations of data in an unsupervised way.These methods have focussed largely on modifying the variational cost function to achieve this goal.However, we show that methods like beta-VAE simplify the tendency of variational inference to underfit causing pathological over-pruning and over-orthogonalization of learned components.In this paper we take a complementary approach: to modify the probabilistic model to encourage structured latent variable representations to be discovered.Specifically, the standard VAE probabilistic model is unidentifiable: the likelihood of the parameters is invariant under rotations of the latent space.This means there is no pressure to identify each true factor of variation with a latent variable.We therefore employ a rich prior distribution, akin to the ICA model, that breaks the rotational symmetry.Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement.The proposed prior allows to improve these approaches with respect to both disentanglement and reconstruction quality significantly over the state of the art.", "target": ["我们提出了在VAEs中无监督地学习分解表征的结构化先验，大大减轻了分解和重建损失之间的权衡。", "使用L^p嵌套分布系列作为VAE的代码向量的先验的一般框架，显示了更高的MIG。", "作者指出了目前VAE方法中的问题，并对VAE、β-VAE和β-TCVAE的重建和正交之间的权衡提供了一个新的视角。"]}
+{"source": "Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks.The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory.We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers.We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small (100k--1.2M parameter) image classification networks.Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts.Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.", "target": ["我们将残余区块泛化为串联区块，使用任意的线性图而不是捷径，并且比ResNets提高了性能。", "本文对ResNet-like架构中的捷径连接进行了分析，并提出用一种被称为串联块的替代卷积来替代身份捷径。", "本文研究了在ResNet中用可训练的卷积跳过连接取代身份跳过连接的效果，发现性能有所提高。"]}
+{"source": "Adam-typed optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning.Such methods are appealing for capability on large-scale sparse datasets.On top of that, they are computationally efficient and insensitive to the hyper-parameter settings.In this paper, we present a new framework for adapting Adam-typed methods, namely AdamT.Instead of applying a simple exponential weighted average, AdamT also includes the trend information when updating the parameters with the adaptive step size and gradients.The newly added term is expected to efficiently capture the non-horizontal moving patterns on the cost surface, and thus converge more rapidly.We show empirically the importance of the trend component, where AdamT outperforms the conventional Adam method constantly in both convex and non-convex settings.", "target": ["我们提出了一个适应Adam类型方法的新框架，即AdamT，在用自适应步长和梯度更新参数时包括趋势信息。", "一种新型的亚当变体，使用霍尔特的线性方法来计算平滑的一阶和二阶动量，而不是使用指数加权平均。"]}
+{"source": "As machine learning methods see greater adoption and implementation in high stakes applications suchas medical image diagnosis, the need for model interpretability and explanation has become morecritical.Classical approaches that assess feature importance (eg saliency maps) do not explain how and why a particular region of an image is relevant to the prediction.We proposea method that explains the outcome of a classification black-box by gradually exaggeratingthe semantic effect of a given class.Given a query input to a classifier, our method produces aprogressive set of plausible variations of that query, which gradually change the posterior probabilityfrom its original class to its negation.These counter-factually generated samples preserve featuresunrelated to the classification decision, such that a user can employ our method as a ``tuning knob'' to traverse a data manifold while crossing the decision boundary.  Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.", "target": ["一种解释分类器的方法，通过夸大或减弱分类器与目标标签相关联的语义特征，产生图像的视觉扰动。", "一个模型，当给一个黑盒输入查询时，旨在通过提供合理的和渐进的查询变化来解释结果，这些变化可以导致输出的变化。", "一种解释图像分类的黑盒输出的方法，该方法对逐渐扰动的输入查询产生逐渐扰动的输出。"]}
+{"source": "We study the problem of explaining a rich class of behavioral properties of deep neural networks.Our influence-directed explanations approach this problem by peering inside the network to identify neurons with high influence on the property of interest using an axiomatically justified influence measure, and then providing an interpretation for the concepts these neurons represent.We evaluate our approach by training convolutional neural networks on Pubfig, ImageNet, and Diabetic Retinopathy datasets.  Our evaluation demonstrates that influence-directed explanations (1) localize features used by the network, (2) isolate features distinguishing related instances, (3) help extract the essence of what the network learned about the class, and (4) assist in debugging misclassifications.", "target": ["我们提出了一种以影响为导向的方法来构建对深度卷积网络行为的解释，并展示了它如何被用来回答先前工作无法解决的一系列问题。", "一种满足某些公理的衡量影响力的方法，以及一个可以用来识别什么输入部分对深度神经网络中的神经元输出最有影响力的影响力概念。", "本文提出测量单个神经元对另一个神经元所代表的感兴趣的数量的影响。"]}
+{"source": "Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily.By contrast, humans have an incredible ability to do one-shot or few-shot learning.For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us.Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data.This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.", "target": ["我们强调了一种技术，通过这种技术，自然语言处理系统可以从上下文中学习一个新词，使其更加灵活。", "一种利用先验知识的技术，以最少的数据学习新词的嵌入表示。"]}
+{"source": "Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning.Here we employed a classic associative inference task from the human neuroscience literature in order to more carefully probe the reasoning capacity of existing memory-augmented architectures.This task is thought to capture the essence of reasoning -- the appreciation of distant relationships among elements distributed across multiple facts or memories.Surprisingly, we found that current architectures struggle to reason over long distance associations.Similar results were obtained on a more complex task involving finding the shortest path between nodes in a path.We therefore developed a novel architecture, MEMO, endowed with the capacity to reason over longer distances.This was accomplished with the addition of two novel components.First, it introduces a separation between memories/facts stored in external memory and the items that comprise these facts in external memory.Second, it makes use of an adaptive retrieval mechanism, allowing a variable number of ‘memory hops’ before the answer is produced.MEMO is capable of solving our novel reasoning tasks, as well as all 20 tasks in bAbI.", "target": ["一个支持推理的内存架构。", "本文提出了对End2End内存网络架构的改变，引入了一个新的配对关联推理任务，大多数现有模型都难以解决这个问题，并表明他们提出的架构能更好地解决这个任务。", "一项来自认知心理学的新任务（成对联想推理），以及对一种新的记忆结构的建议，该结构的特点是允许在成对联想任务上有更好的表现。"]}
+{"source": "Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency.They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures).Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results.In this work, we study how depthwise separable convolutions can be applied to neural machine translation.We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves better results.In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation.We also introduce a new super-separable convolution operation that further reduces the number of parameters and computational cost of the models.", "target": ["深度可分离卷积改善神经机器翻译：可分离性越强越好。", "本文提出在全卷积神经机器翻译模型中使用深度可分离卷积层，并引入了一个新的超可分离卷积层，进一步降低了计算成本。"]}
+{"source": "Interpreting generative adversarial network (GAN) training as approximate divergence minimization has beentheoretically insightful, has spurred discussion, and has lead to theoretically and practically interestingextensions such as f-GANs and Wasserstein GANs.For both classic GANs and f-GANs, there is an original variant of training and a \"non-saturating\" variant which uses an alternative form of generator gradient.The original variant is theoretically easier to study, but for GANs the alternative variant performs better in practice.The non-saturating scheme is often regarded as a simple modification to deal with optimization issues, but we show that in fact the non-saturating scheme for GANs is effectively optimizing a reverse KL-like f-divergence.We also develop a number of theoretical tools to help compare and classify f-divergences.We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.", "target": ["非饱和的GAN训练有效地减少了类似KL的反向f-散度。", "本文提出了f-散度���的有用表达方式，从新开发的工具中研究了流行的f-散度的理论属性，并研究了具有非饱和训练方案的GANs。"]}
+{"source": "We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems.We apply the technique to entity disambiguation of inventor names in US patents.The method involves converting text from each pairwise comparison between two inventor name records into a 2D RGB (stacked) image representation.We then train an image classification neural network to discriminate between such pairwise comparison images, and use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors), obtaining highly accurate results (F1: 99.09%, precision: 99.41%, recall: 98.76%).Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, such as disambiguation of academic publications, or for problems that require simultaneous classification of both text and images.", "target": ["我们引入了一种新的文本表示方法，使图像分类器能够应用于文本分类问题，并将该方法应用于发明人姓名消歧。", "一种将一对文本信息映射到二维RGB图像的方法，可将其输入二维卷积神经网络（图像分类器）。", "作者考虑了专利名称发明人的名称消歧问题，并提议建立两个名称字符串的图像页面表示，以进行比较并应用图像分类器。"]}
+{"source": "We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN.DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.Suppose there are two distributions  $p_{\\bar{d}}$ and $p_{d}$ such that the density of the target distribution can be the differences between the densities of $p_{\\bar{d}}$ and $p_{d}$.We show how to learn the target distribution $p_{t}$ only via samples from $p_{d}$ and $p_{\\bar{d}}$ (relatively easy to obtain).DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution).Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN.We also provide theoretical analyses about the convergence of DSGAN.", "target": ["我们提出了 \"差集生成对抗网络\"（DSGAN）模型来学习难以收集训练数据的目标分布。", "本文介绍了DS-GAN，其目的是学习样本难以或无法收集的任何两个分布之间的差异，并展示了其在半监督学习和对抗性训练任务上的有效性。", "本文考虑了学习GAN来捕捉目标分布的问题，而该分布中只有很少的训练样本可用。"]}
+{"source": "Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner.However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice.Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients.To tackle this problem, we propose a GuideGAN based on attention mechanism.More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction.This attention map then assists the generator to produce more plausible and realistic images.We extensively evaluate the proposed GuideGAN framework on a  number of image transfer tasks.Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.", "target": ["一种通过使用注意力嵌入判别器提高GAN框架的图像翻译性能的通用方法", "GAN框架中的一个反馈机制，在图像与图像之间的转换中提高了生成图像的质量，其判别器输出一张地图，指出生成器应该关注的地方，使其结果更有说服力。", "提出了一个具有基于注意力的I2I翻译判别器的GAN，它提供了真/假的概率和反映图像生成的显着性的注意力图。"]}
+{"source": "The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation.However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains unexplored.This paper specifically aims to study the fact verification given semi-structured data as evidence.To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.TabFact is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA).Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification.LPA parses statements into LISP-like programs and executes them against the tables to obtain the returned binary value for verification.Both methods achieve similar accuracy but still lag far behind human performance.We also perform a comprehensive analysis to demonstrate great future opportunities.", "target": ["我们提出一个新的数据集来研究半结构化表下的蕴含问题，作为前提条件", "本文提出了一个新的数据集，用于基于表的事实验证，并介绍了任务的方法。", "作者提出了用表格等半结构化数据源进行事实核查的问题，创建了一个新的数据集，并对有变化的基线模型进行了评估。"]}
+{"source": "This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes.Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs.We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process.Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently.We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art.", "target": ["我们开发了一个深度图谱匹配架构，它改进了最初的对应关系，以达成邻里共识。", "一个回答图匹配问题的框架，包括本地节点嵌入和消息传递完善步骤。", "一个基于GNN的两阶段架构，用于建立两个图之间的对应关系，在图像匹配和知识图实体联合的现实世界任务中表现良好。"]}
+{"source": "This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces.  The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer.This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.", "target": ["本文将欧几里得空间上的连续（甚至是可测）函数空间中的神经网络密度证明扩展到概率度量的紧凑集合上的函数。", "本文研究了为解决多实例学习问题而设计的一系列神经网络的近似特性，并表明标准的单层结构的结果可以扩展到这些模型。", "本文将通用近似定理归纳到测量空间上的实函数。"]}
+{"source": "Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.We propose Mahé, a novel approach to provide Model-Agnostic Hierarchical Explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances.Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors.Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully provides explanations of interactions that are context-free.", "target": ["预测的上下文依赖和无上下文解释的新框架", "作者扩展了用于解释黑箱模型的线性区域归因方法LIME，并提出了一种辨别上下文依赖性和无上下��互动的方法。", "一种能够为模型提供分层解释的方法，包括通过区域解释算法提供上下文相关的解释和无上下文解释。"]}
+{"source": "To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency.  To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision.  Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models.We also demonstrate ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks -- the highest scores to date.Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary.We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise.The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates.  By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing.  Sensitivity analysis indicates that these techniques, coupled with proper activation function range calibration, offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.", "target": ["量化后的微调与8位元和4位元量化相匹配或超过全精度最先进的网络。", "本文提出通过对预训练的模型做量化处理，使用大的批次规模，以及使用适当的学习率退火法，延长训练时间，来提高低精度模型的性能。", "一种在高效硬件上进行推理的低位元量化方法，在ResNet50上用4位元权重和激活实现了完全的准确性，其依据是观察到低精度的微调会在梯度上引入噪音。"]}
+{"source": "Analysis methods which enable us to better understand the  representations and functioning of neural models of language are  increasingly needed as deep learning becomes the dominant approach  in NLP.Here we present two methods based on Representational  Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to  directly quantify how strongly the information encoded in neural  activation patterns corresponds to information represented by  symbolic structures such as syntax trees.We first validate our  methods on the case of a simple synthetic language for arithmetic  expressions with clearly defined syntax and semantics, and show that  they exhibit the expected pattern of results.We then apply our methods to  correlate neural representations of English sentences with their  constituency parse trees.", "target": ["两种基于表征相似性分析（RSA）和树核（TK）的方法，直接量化了神经激活模式中编码的信息与符号结构所代表的信息之间的对应程度。"]}
+{"source": "Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance.However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples.In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation.To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process.The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task.On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of our framework.", "target": ["本文介绍了一个通过潜在空间的自适应抽样来进行数据高效表���学习的框架。", "一种顺序和适应性地选择要提交给训练算法的训练实例的方法，其中选择发生在潜空间，基于选择损失梯度方向的样本。", "一种在神经网络训练中有效选择硬样本的方法，通过将样本编码到潜空间的变异自动编码器来实现。"]}
+{"source": "Existing methods for AI-generated artworks still struggle with generating high-quality stylized content, where high-level semantics are preserved, or separating fine-grained styles from various artists.We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of variations when only one of them is labelled in general, and fully decompose complex anime illustrations into style and content in particular.Training such model is challenging, since given a style, various content data may exist but not the other way round.Our approach is divided into two stages, one that encodes an input image into a style independent content, and one based on a dual-conditional generator.We demonstrate the ability to generate high-fidelity anime portraits with a fixed content and a large variety of styles from over a thousand artists, and vice versa, using a single end-to-end network and with applications in style transfer.We show this unique capability as well as superior output to the current state-of-the-art.", "target": ["一种基于对抗性训练的方法，用于区分数据集中两组互补的变化，其中只有一个被标记，在动漫插图的风格与内容上进行测试。", "一种结合条件GANs和条件VAEs的图像生成方法，可生成具有不同艺术家的各种风格的高保真动漫图像。", "建议采用一种方法来学习动漫中分解的风格（艺术家）和内容表征。"]}
+{"source": "Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.Inspired by the intuition that humans are more sensitive to the lower-frequency (larger-scale) patterns we design a regularization scheme that penalizes large differences between adjacent components within each convolutional kernel.We apply our regularization onto several popular training methods, demonstrating that the models with the proposed smooth kernels enjoy improved adversarial robustness.Further, building on recent work establishing connections between adversarial robustness and interpretability, we show that our method appears to give more perceptually-aligned gradients.", "target": ["我们为CNN的卷积核引入了一种平滑度正则化，它可以帮助提高对抗性的稳定性，并导致感知上的连结梯度。", "本文提出了一种新的正则化方案，鼓励卷积核更加平滑，认为减少神经网络对高频成分的依赖有助于对对抗性实例的稳定性。", "作者提出了一种学习更平滑的卷积核的方法，具体来说，就是用惩罚使用高频输入成分的直觉来惩罚核的连续像素之间的巨大变化的正则器。"]}
+{"source": "Despite an ever growing literature on reinforcement learning algorithms and applications, much less is known about their statistical inference.In this paper, we investigate the large-sample behaviors of the Q-value estimates with closed-form characterizations of the asymptotic variances.This allows us to efficiently construct confidence regions for Q-value and optimal value functions, and to develop policies to minimize their estimation errors.This also leads to a policy exploration strategy that relies on estimating the relative discrepancies among the Q estimates.Numerical experiments show superior performances of our exploration strategy than other benchmark approaches.", "target": ["我们研究了Q值估计的大样本行为，并提出了一个有效的探索策略，该策略依赖于对Q值估计之间的相对差异的估计。"]}
+{"source": "Entailment vectors are a principled way to encode in a vector what information is known and what is unknown.  They are designed to model relations where one vector should include all the information in another vector, called entailment.  This paper investigates the unsupervised learning of entailment vectors for the semantics of words.  Using simple entailment-based models of the semantics of words in text (distributional semantics), we induce entailment-vector word embeddings which outperform the best previous results for predicting entailment between words, in unsupervised and semi-supervised experiments on hyponymy.", "target": ["我们基于尾随性而不是相似性来训练词的嵌入，成功地预测了词汇的蕴含。", "本文提出了一种词汇嵌入算法，用于词汇的蕴含，该算法遵循Henderson和Popa（ACL，2016）的工作。"]}
+{"source": "We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner.Our scheme pits two versions of the same agent, Alice and Bob, against one another.Alice proposes a task for Bob to complete; and then Bob attempts to complete the task.  In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset.Alice will \"propose\" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively.  Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent.When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.", "target": ["利用自我发挥的自动课程进行强化学习的无监督学习", "一种以无监督的方式探索环境的新表述，以帮助以后的特定任务，其中一个代理提出越来越难的任务，学习的代理试图完成这些任务。", "一个自我游戏模型，其中一个代理学习提出对他们来说容易但对对手来说困难的任务，创造一个自我游戏目标和学习课程的移动目标。"]}
+{"source": "Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction.The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN's) for graph data processing.Recently, graph attention network (GAT) has proven a promising attempt by combining graph neural networks with attention mechanism, so as to achieve massage passing in graphs with arbitrary structures.However, the attention in GAT is computed mainly based on the similarity between the node content, while the structures of the graph remains largely unemployed (except in masking the attention out of one-hop neighbors).In this paper, we propose an `````````````````````````````\"ADaptive Structural Fingerprint\" (ADSF) model to fully exploit both topological details of the graph and  content features of the nodes.The key idea is to contextualize each node with a weighted, learnable receptive field  encoding rich and diverse local graph structures.By doing this, structural interactions between the nodes can  be inferred accurately, thus improving subsequent attention layer as well as the convergence of learning.Furthermore, our model provides a useful platform  for different subspaces of node features and various scales of graph structures to ``cross-talk'' with each other through the learning of multi-head attention, being particularly useful in handling complex real-world data.  Encouraging performance is observed on a number of benchmark data sets in node classification.", "target": ["通过自适应的 \"结构指纹 \"开发图结构数据中丰富的结构细节", "一种基于图结构的方法来增强图神经网络的注意机制，其主要想法是探索根节点的本地邻居的不同类型的节点之间的相互作用。", "本文扩展了图NN中的自我注意的想法，它通常是基于节点之间的特征相似性，包括结构相似性。"]}
+{"source": "Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people.We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs).While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces.We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve.We split the challenge into two simpler components.First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy.Second, we train a Bayesian residual policy to improve upon the ensemble's recommendation and learn to reduce uncertainty.Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models.BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.", "target": ["我们提出了一种可扩展的贝叶斯强化学习算法，该算法在有洞察力专家的集合上学习贝叶斯修正，以解决具有复杂潜在奖励和动态的问题。", "本文考虑了贝叶斯强化学习问题，在潜在的马尔科夫决策过程（MDPs）上，透过与专家进行决策。", "在本文中，作者激励并提出了一种针对贝叶斯强化学习问题的学习算法，称为贝叶斯剩余策略优化（BRPO）。"]}
+{"source": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth.This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks.For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function.Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum.We believe these insights are also useful in analyzing deep models and other first order methods.", "target": ["我们证明梯度下降在过度参数化的神经网络上以线性速率实现了零训练损失。", "这项工作考虑优化一个具有平方损失的两层超参数化ReLU网络，并给定一个具有任意标签的数据集。", "本文研究了具有平方损失的一隐层神经网络，他们表明在过度参数化的设置中，随机初始化和梯度下降得到的损失为零。"]}
+{"source": "For many applications, in particular in natural science, the task is todetermine hidden system parameters from a set of measurements.Often,the forward process from parameter- to measurement-space is well-defined,whereas the inverse problem is ambiguous: multiple parameter sets canresult in the same measurement.To fully characterize this ambiguity, the fullposterior parameter distribution, conditioned on an observed measurement,has to be determined.We argue that a particular class of neural networksis well suited for this task – so-called Invertible Neural Networks (INNs).Unlike classical neural networks, which attempt to solve the ambiguousinverse problem directly, INNs focus on learning the forward process, usingadditional latent output variables to capture the information otherwiselost.Due to invertibility, a model of the corresponding inverse process islearned implicitly.Given a specific measurement and the distribution ofthe latent variables, the inverse pass of the INN provides the full posteriorover parameter space.We prove theoretically and verify experimentally, onartificial data and real-world problems from medicine and astrophysics, thatINNs are a powerful analysis tool to find multi-modalities in parameter space,uncover parameter correlations, and identify unrecoverable parameters.", "target": ["用可逆神经网络分析逆向问题", "作者提出使用可逆网络来解决模糊的逆向问题，并建议不仅要训练前向模型，还要训练具有MMD批判的逆向模型。", "该研究论文提出了一种可逆网络，其观测值为复杂输入分布的后验概率，具有理论上有效的双向训练方案。"]}
+{"source": "Decisions made by machine learning systems have increasing influence on the world.Yet it is common for machine learning algorithms to assume that no such influence exists.An example is the use of the i.i.d.assumption in online learning for applications such as content recommendation, where the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users.Generally speaking, it is possible for an algorithm to change the distribution of its own inputs.We introduce the term self-induced distributional shift (SIDS) to describe this phenomenon.A large body of work in reinforcement learning and causal machine learning aims to deal with distributional shift caused by deploying learning systems previously trained offline.Our goal is similar, but distinct: we point out that changes to the learning algorithm, such as the introduction of meta-learning, can reveal hidden incentives for distributional shift (HIDS), and aim to diagnose and prevent problems associated with hidden incentives.We design a simple  environment as a \"unit test\" for HIDS, as well as a content recommendation environment which allows us to disentangle different types of SIDS.  We demonstrate the potential for HIDS to cause unexpected or undesirable behavior in these environments, and propose and test a mitigation strategy.Â", "target": ["绩效指标是不完整的规范；目的并不总是证明方法的正确性。", "作者展示了元学习如何揭示了分布式转变的隐藏动机，并提出了一种基于在不同环境中交换学习者以减少自我引入分布式转变的方法。", "本文通过在元学习中使任务变得更容易，将学习者获胜的内在动机概括为更多类的问题。"]}
+{"source": "In one-class-learning tasks, only the normal case can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently by samples.Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.However, generative models suffer from a large input dimensionality (as in images) and are typically inefficient learners.We propose to learn the data distribution more efficiently with a multi-hypotheses autoencoder.Moreover, the model is criticized by a discriminator, which prevents artificial data modes not supported by data, and which enforces diversity across hypotheses.This consistency-based anomaly detection (ConAD) framework allows the reliable identification of outof- distribution samples.For anomaly detection on CIFAR-10, it yields up to 3.9% points improvement over previously reported results.On a real anomaly detection task, the approach reduces the error of the baseline models from 6.8% to 1.5%.", "target": ["我们提出了一种异常检测方法，该方法将通过多个区域密度对前景类进行建模与对抗性训练相结合。", "本文提出了一种技术，通过使生成模型与区域密度相一致而使其更加稳定。"]}
+{"source": "Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data.In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN).First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process.A key component of our method is that we train a posterior inference network for the hidden variables.Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms.We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN.We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods.We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.", "target": ["我们提出了一个GAN变体，它可以学习生成点云。不同的研究已经被探索，包括更紧密的Wasserstein距离估计、条件生成、对未见过的点云的泛化以及图像到点云。", "本文提出使用GAN来生成3D点云，并引入了一个夹层目标，平均分配之间的Wasserstein距离的上下限。", "本文提出了一个新的无序数据的生成模型，特别适用于点云，其中包括一个推理方法和一个新的目标函数。"]}
+{"source": "Existing attention mechanisms, are mostly item-based in that a model is trained to attend to individual items in a collection (the memory) where each item has a predefined, fixed granularity, e.g., a character or a word.Intuitively, an area in the memory consisting of multiple items can be worth attending to as a whole.We propose area attention: a way to attend to an area of the memory, where each area contains a group of items that are either spatially adjacent when the memory has a 2-dimensional structure, such as images, or temporally adjacent for 1-dimensional memory, such as natural language sentences.Importantly, the size of an area, i.e., the number of items in an area or the level of aggregation, is dynamically determined via learning, which can vary depending on the learned coherence of the adjacent items.By giving the model the option to attend to an area of items, instead of only individual items, a model can attend to information with varying granularity.Area attention can work along multi-head attention for attending to multiple areas in the memory.We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases.These improvements are obtainable with a basic form of area attention that is parameter free.In addition to proposing the novel concept of area attention, we contribute an efficient way for computing it by leveraging the technique of summed area tables.", "target": ["本文提出了一种新的注意力机制的方法，可以使一系列的任务受益，如机器翻译和图像标题。", "本文将目前的注意力模型从单词层面扩展到相邻单词的组合，将模型应用于由相邻单词合并而成的项目。"]}
+{"source": "We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters.To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search.Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.", "target": ["我们确定了一种现象，即神经洗脑，并引入了统计学上合理的重量可塑性损失来克服这种现象。", "本文讨论了 \"神经洗脑 \"的现象，它指的是一个模型的性能通过另一个模型共享模型参数而受到影响。"]}
+{"source": "Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations.To address this issue we introduce Morpho-MNIST, a framework that aims to answer: \"to what extent has my model learned to represent specific factors of variation in the data?\"We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity.We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation.", "target": ["本文介绍了Morpho-MNIST，这是一个形状度量和扰动的集合，是对表征学习进行定量评估的一个步骤。", "本文讨论了评估和诊断使用生成模型学习的代表的问题。", "作者提出了一套对MNISt数据集进行分类的标准和一套有趣的扰动来修改MNIST数据集。"]}
+{"source": "Exploration in environments with sparse rewards is a key challenge for reinforcement learning.How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy?We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well.We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning.These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards.We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.", "target": ["通过无监督的视觉抽象发现和控制进行深度强化学习的结构化探索", "本文介绍了用于强化学习的视觉抽象，其中一个算法学习 \"控制 \"每个抽象，以及选择选项以实现整体任务。"]}
+{"source": "Combinatorial optimization is a common theme in computer science.While in general such problems are NP-Hard, from a practical point of view, locally optimal solutions can be useful.In some combinatorial problems however, it can be hard to define meaningful solution neighborhoods that connect large portions of the search space, thus hindering methods that search this space directly.We suggest to circumvent such cases by utilizing a policy gradient algorithm that transforms the problem to the continuous domain, and to optimize a new surrogate objective that renders the former as generic stochastic optimizer.This is achieved by producing a surrogate objective whose distribution is fixed and predetermined, thus removing the need to fine-tune various hyper-parameters in a case by case manner.Since we are interested in methods which can successfully recover locally optimal solutions, we use the problem of finding locally maximal cliques as a challenging experimental benchmark, and we report results on a large dataset of graphs that is designed to test clique finding algorithms.Notably, we show in this benchmark that fixing the distribution of the surrogate is key to consistently recovering locally optimal solutions, and that our surrogate objective leads to an algorithm that outperforms other methods we have tested in a number of measures.", "target": ["一种新的策略梯度算法，旨在处理黑盒子组合优化问题。该算法仅依赖于函数评估，并以高概率返回区域最优解。", "本文提出了一种构建代理目标的方法，用于将政策梯度方法应用于组合优化，目的是减少对超参数调整的需求。", "本文提出用政策梯度算法中的奖励项替换其中心的经验累积分布。"]}
+{"source": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial.While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions.In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.We accomplish this by  placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output.Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters.We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.", "target": ["无需取样的神经网络的快速、校准的不确定性估计", "本文提出了一种新的方法来估计回归环境中预测的信赖度，为具有完全整合不确定性估计的在线应用打开了大门。", "本文提出了深度证据回归，这是一种训练神经网络的方法，不仅要估计输出，还要估计支持该输出的相关证据。"]}
+{"source": "The Lottery Ticket Hypothesis from Frankle & Carbin (2019) conjectures that, for typically-sized neural networks, it is possible to find small sub-networks which train faster and yield superior performance than their original counterparts.The proposed algorithm to search for such sub-networks (winning tickets), Iterative Magnitude Pruning (IMP), consistently finds sub-networks with 90-95% less parameters which indeed train faster and better than the overparameterized models they were extracted from, creating potential applications to problems such as transfer learning.In this paper, we propose a new algorithm to search for winning tickets, Continuous Sparsification, which continuously removes parameters from a network during training, and learns the sub-network's structure with gradient-based methods instead of relying on pruning strategies.We show empirically that our method is capable of finding tickets that outperforms the ones learned by Iterative Magnitude Pruning, and at the same time providing up to 5 times faster search, when measured in number of training epochs.", "target": ["我们提出了一种新的算法，可以快速找到神经网络中的中奖票。", "本文提出了一个新的目标函数，可用于联合优化分类目标，同时鼓励网络中的稀疏化，表现出高精确度。", "这项工作提出了一种新的迭代修剪方法，即连续稀疏化（Continuous Sparsification），它不断修剪当前权重，直到达到目标比率。"]}
+{"source": "In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence.However, the growing complexity of machine learning datasets and models may violate such assumptions.Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints.Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training.We analyze the following problem: \"given a dataset, algorithm, and fixed resource budget, what is the best achievable performance?\"We focus on the number of optimization iterations as the representative resource.Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget.Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing.We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation).We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget.We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.", "target": ["引入预算培训的正式设置，并提出一个预算意识的线性学习率时间表", "这项工作在固定的时期数下，提出了一种调整神经网络训练的学习率的技术。", "本文利用引入的BAS（Budget-Aware Schedule）概念，分析了当迭代次数有限时，应该使用哪种学习率计划。"]}
+{"source": "We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives.Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational space to gauge novelty.We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space.One key element of our approach is that we perform more gradient steps in-between every environment step in order to ensure the model accuracy.We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines.", "target": ["我们使用内在的奖励进行探索，这种奖励是基于表征空间中最近的邻居的加权距离。", "本文提出了一种在表格MDP中进行有效探索的方法，以及一个简单的控制环境，使用确定性编码器来学习环境动态的低维表示。", "本文提出了一种使用基于模型和无模型的方法结合新的度量的RL代理的样本高效探索方法。"]}
+{"source": "Neural networks are vulnerable to small adversarial perturbations.While existing literature largely focused on the vulnerability of learned models, we demonstrate an intriguing phenomenon that adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarially trained model that is both trained and evaluated on the new distribution.We show this by constructing semantically- identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve similar clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.", "target": ["PGD训练的模型的稳定性表现对图像数据集的语义保存转换很敏感，这意味着在实践中对稳定学习算法的评估很棘手。"]}
+{"source": "Sample inefficiency is a long-lasting problem in reinforcement learning (RL).  The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization.Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions.  To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles.These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency.We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.", "target": ["我们提出排名政策梯度，学习行动的最佳排名以实现收益最大化。我们提出了一个通用的非政策学习框架，该框架具有保存最优性、减少方差和样本效率的特性。", "本文提出使用一种排名形式对策略进行重新参数化，将RL问题转化为监督学习问题。", "本文从排名的角度对政策梯度方法提出了一个新的观点。"]}
+{"source": "We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition.MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss.The self-supervised loss only uses data augmentation and thus does not require additional labels.Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy.When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method.The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution.An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.", "target": ["将分类和图像检索结合在一个神经网络架构中，我们获得了对这两项任务的改进。", "本文提出了一种用于图像分类和实例检索的统一嵌入，以提高这两项任务的性能。", "本文提出要联合训练一个深度神经网，用于图像分类、实例和复制识别。"]}
+{"source": "In this paper, we investigate mapping the hyponymy relation of wordnet to feature vectors.  We aim to model lexical knowledge in such a way that it can be used as  input in generic machine-learning models, such as phrase entailment  predictors.  We propose two models.The first one leverages an existing mapping of  words to feature vectors (fasttext), and attempts to classify  such vectors as within or outside of each class.The second model is fully supervised,  using solely wordnet as a ground truth.It maps each concept to an  interval or a disjunction thereof.  On the first model, we approach, but not quite attain state of the  art performance.The second model can achieve near-perfect accuracy.", "target": ["我们调查了将词网的连词关系映射到特征向量的情况", "本文研究词与词之间的连词如何能被映射到特征表征上。", "本文探讨了词向量表征中的连词概念，并描述了一种将词网关系组织成树状结构来定义连词的方法。"]}
+{"source": "Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.   Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.  We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.  Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.", "target": ["我们通过鉴别性地训练评分函数，根据好的写作的各种品质对候选代进行排名，从而建立一个更强大的自然语言产生器。", "本文提出将多个希望纠正序列解码中不一致的归纳性偏差汇集在一起，并提出对预先确定的各种子目标组合的参数进行优化。", "本文将RNN语言模型与几个经过判别训练的模型结合起来，以改善语言的生成。", "本文提出使用受Grice的交流格言启发的增强型目标来改进RNN语言模型的生成。"]}
+{"source": "In recent years, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure with increasing levels of server heterogeneity and increasing size of cloud services and applications.In such many software-load-balancers heterogeneous systems, traditional solutions, such as JSQ, incur an increasing communication overhead, whereas low-communication alternatives, such as JSQ(d) and the recently proposed JIQ scheme are either unstable or provide poor performance.We argue that a better low-communication load balancing scheme can be established by allowing each dispatcher to have a different view of the system and keep using JSQ, rather than greedily trying to avoid starvation on a per-decision basis. accordingly, we introduce the Loosely-Shortest -Queue family of load balancing algorithms.Roughly speaking, in Loosely-shortest -Queue, each dispatcher keeps a different approximation of the server queue lengths and routes jobs to the shortest among them.Communication is used only to update the approximations and make sure that they are not too far from the real queue lengths in expectation.We formally establish the strong stability of any Loosely-Shortest -Queue policy and provide an easy-to-verify sufficient condition for verifying that a policy is Loosely-Shortest -Queue.We further demonstrate that the Loosely-Shortest -Queue approach allows constructing throughput optimal policies with an arbitrarily low communication budget.Finally, using extensive simulations that consider homogeneous, heterogeneous and highly skewed heterogeneous systems in scenarios with a single dispatcher as well as with multiple dispatchers, we show that the examined Loosely-Shortest -Queue example policies are always stable as dictated by theory.Moreover, it exhibits an appealing performance and significantly outperforms well-known low-communication policies, such as JSQ(d) and JIQ, while using a similar communication budget.", "target": ["异质服务器多分配器系统的可扩展和低通信负载平衡解决方案，具有强大的理论保证和有希望的经验结果。"]}
+{"source": "We propose a novel quantitative measure to predict the performance of a deep neural network classifier, where the measure is derived exclusively from the graph structure of the network.We expect that this measure is a fundamental first step in developing a method to evaluate new network architectures and reduce the reliance on the computationally expensive trial and error or \"brute force\" optimisation processes involved in model selection.The measure is derived in the context of multi-layer perceptrons (MLPs), but the definitions are shown to be useful also in the context of deep convolutional neural networks (CNN), where it is able to estimate and compare the relative performance of different types of neural networks, such as VGG, ResNet, and DenseNet.Our measure is also used to study the effects of some important \"hidden\" hyper-parameters of the DenseNet architecture, such as number of layers, growth rate and the dimension of 1x1 convolutions in DenseNet-BC.Ultimately, our measure facilitates the optimisation of the DenseNet design, which shows improved results compared to the baseline.", "target": ["预测深度神经网络模型性能的量化方法。", "本文提出了一个新的数量，即计算神经网络中的路径数量，这对具有相同参数数量的神经网络的性能具有预测作用。", "本文提出了一种计算深度神经网络中路径的方法，可以说是用来衡量网络的性能。"]}
+{"source": "There is a stark disparity between the learning rate schedules used in the practice of large scale machine learning and what are considered admissible learning rate schedules prescribed in the theory of stochastic approximation.Recent results, such as in the 'super-convergence' methods which use oscillating learning rates, serve to emphasize this point even more.One plausible explanation is that non-convex neural network training procedures are better suited to the use of fundamentally different learning rate  schedules, such as the ``cut the learning rate every constant number of epochs'' method (which more closely resembles an exponentially decaying learning rate schedule); note that this widely used schedule is in stark contrast to the polynomial decay schemes prescribed in the stochastic approximation literature, which are indeed shown to be (worst case) optimal for classes of convex optimization problems.The main contribution of this work shows that the picture is far more nuanced, where we do not even need to move to non-convex optimization to show other learning rate schemes can be far more effective.In fact, even for the simple case of stochastic linear regression with a fixed time horizon, the rate achieved by any polynomial decay scheme is sub-optimal compared to the statistical minimax rate (by a factor of condition number); in contrast the ```''cut the learning rate every constant number of epochs'' provides an exponential improvement (depending only logarithmically on the condition number) compared to any polynomial decay scheme.  Finally, it is important to ask if our theoretical insights are somehow fundamentally tied to quadratic loss minimization (where we have circumvented minimax lower bounds for more general convex optimization problems)?Here, we conjecture that recent results which make the gradient norm small at a near optimal rate, for both convex and non-convex optimization, may also provide more insights into learning rate schedules used in practice.", "target": ["本文提出了一个严格的研究，即为什么实际使用的学习率计划（对于给定的计算预算）具有显着的优势，尽管这些计划并不被随机逼近的经典理论所提倡。", "本文对不同的学习率计划进行了理论研究，得出了多项式和常数和切割方案的统计学最低下限。", "本文研究了学习率选择对随机优化的影响，重点是具有衰减步长的最小均方。"]}
+{"source": "We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments.We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems.We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.", "target": ["我们提出了基于Convnets的规划器，这些规划器具有样本效率，并且可以推广到更大的导航和寻路问题的实例中。", "提出了一些方法，这些方法可以看作是对价值迭代网络（VIN）的修改，并进行了一些改进，旨在提高样本效率和对大环境规模的泛化。", "本文通过考虑与状态有关的转换函数，提出了对原有价值迭代网络（VIN）的扩展。"]}
+{"source": "Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks.On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks.On the other hand, many domain-specific tasks do not have a large enough domain corpus to obtain high-quality embeddings.We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.In this paper, we formulate the learning of word embeddings as a lifelong learning process.Given knowledge learned from many previous domains and a small new domain corpus, the proposed method can effectively generate new domain embeddings by leveraging a simple but effective algorithm and a meta-learner, where the meta-learner is able to provide word context similarity information at the domain-level.Experimental results demonstrate that the proposed method can effectively learn new domain embeddings from a small corpus and past domain knowledges\\footnote{We will release the code after final revisions.}.Wealso demonstrate that general-purpose embeddings trained from a large scale corpus are sub-optimal in domain-specific tasks.", "target": ["通过终身学习和元学习来学习更好的领域嵌入", "提出了一种学习词嵌入的终身学习方法。", "本文提出了一种在新领域中学习嵌入的方法，并在一个方面的提取任务上大大超过了基线。"]}
+{"source": "Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss.Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fact that the expressiveness of CNNs is fragile and needs a more gentle way of regularization for the networks to adapt during pruning.To solve this problem, we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance, whose effectiveness is proved on popular CNNs compared with state-of-the-art methods.", "target": ["我们提出了一种新的基于正则化的修剪方法（名为IncReg），根据不同权重组的相对重要性，逐步将不同的正则化因子分配给不同的权重组。", "我们提出了一种新的基于正则化的修剪方法（名为IncReg），根据不同权重组的相对重要性，逐步将不同的正则化因子分配给不同的权重组。"]}
+{"source": "Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning models, as they often provide significant improvements over stochastic gradient descent (SGD).Rigorously speaking, fast gradient methods have provable improvements over gradient descent only for the deterministic case, where the gradients are exact.In the stochastic case, the popular explanations for their wide applicability is that when these fast gradient methods are applied in the stochastic case, they partially mimic their exact gradient counterparts, resulting in some practical gain.This work provides a counterpoint to this belief by proving that there exist simple problem instances where these methods cannot outperform SGD despite the best setting of its parameters.These negative problem instances are, in an informal sense, generic; they do not look like carefully constructed pathological instances.These results suggest (along with empirical evidence) that HB or NAG's practical performance gains are a by-product of minibatching.Furthermore, this work provides a viable (and provable) alternative, which, on the same set of problem instances, significantly improves over HB, NAG, and SGD's performance.This algorithm, referred to as Accelerated Stochastic Gradient Descent (ASGD), is a simple to implement stochastic algorithm, based on a relatively less popular variant of Nesterov's Acceleration.Extensive empirical results in this paper show that ASGD has performance gains over HB, NAG, and SGD.The code for implementing the ASGD Algorithm can be found at https://github.com/rahulkidambi/AccSGD.", "target": ["现有的动量/加速方案，如重球法和Nesterov加速法与随机梯度一起使用，并没有比虚无缥缈的随机梯度下降法有所改善，尤其是在使用小批量的时候。"]}
+{"source": "Oversubscription planning (OSP) is the problem of finding plans that maximize the utility value of their end state while staying within a specified cost bound.Recently, it has been shown that OSP problems can be reformulated as classical planning problems with multiple cost functions but no utilities.  Here we take advantage of this reformulation to show that OSP problems can be solved optimally using the A* search algorithm, in contrast to previous approaches that have used variations on branch-and-bound search.This allows many powerful techniques developed for classical planning to be applied to OSP problems.We also introduce novel bound-sensitive heuristics, which are able to reason about the primary cost of a solution while taking into account secondary cost functions and bounds, to provide superior guidance compared to heuristics that do not take these bounds into account.We implement two such bound-sensitive variants of existing classical planning heuristics, and show experimentally that the resulting search is significantly more informed than comparable heuristics that do not consider bounds.", "target": ["我们表明，超额认购规划任务可以用A*来解决，并为超额认购规划任务引入了新的边界敏感启发式方法。", "提出了一种通过使用多成本函数转换为经典规划的方法来优化解决超额订购规划（OSP）任务。", "本文提出了对可接受的启发式方法的修改，使其在多标准的环境下更有依据，其中。"]}
+{"source": "Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures.  On the other hand, few-shot learning methods are highly vulnerable to adversarial examples.  The goal of our work is to produce networks which both perform well at few-shot tasks and are simultaneously robust to adversarial examples.  We adapt adversarial training for meta-learning, we adapt robust architectural features to small networks for meta-learning, we test pre-processing defenses as an alternative to adversarial training for meta-learning, and we investigate the advantages of robust meta-learning over robust transfer-learning for few-shot tasks.  This work provides a thorough analysis of adversarially robust methods in the context of meta-learning, and we lay the foundation for future work on defenses for few-shot tasks.", "target": ["我们为对抗性稳定的少量学习开发了元学习方法。", "本文提出了一种方法，通过在元学习算法的内部任务微调阶段引入对抗性查询数据攻击，增强了少量样本学习的稳定性。", "本文作者提出了一种新的方法来训练一个健壮的低样本模型。"]}
+{"source": "Many of our core assumptions about how neural networks operate remain empirically untested.One common assumption is that convolutional neural networks need to be stable to small translations and deformations to solve image recognition tasks.For many years, this stability was baked into CNN architectures by incorporating interleaved pooling layers.Recently, however, interleaved pooling has largely been abandoned.This raises a number of questions: Are our intuitions about deformation stability right at all?Is it important?Is pooling necessary for deformation invariance?If not, how is deformation invariance achieved in its absence?In this work, we rigorously test these questions, and find that deformation stability in convolutional networks is more nuanced than it first appears: (1) Deformation invariance is not a binary property, but rather that different tasks require different degrees of deformation stability at different layers.(2) Deformation stability is not a fixed property of a network and is heavily adjusted over the course of training, largely through the smoothness of the convolutional filters.(3) Interleaved pooling layers are neither necessary nor sufficient for achieving the optimal form of deformation stability for natural image classification.(4) Pooling confers \\emph{too much} deformation stability for image classification at initialization, and during training, networks have to learn to \\emph{counteract} this inductive bias.Together, these findings provide new insights into the role of interleaved pooling and deformation invariance in CNNs, and demonstrate the importance of rigorous empirical testing of even our most basic assumptions about the working of neural networks.", "target": ["我们发现，仅靠池化并不能决定CNN的变形稳定性，而滤波器的平滑度在决定稳定性方面起着重要作用。"]}
+{"source": "Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training.Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels.For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs.We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch.While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss.We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios.It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.", "target": ["我们提出了一个自我组装框架，以在有噪音的标记数据集下训练更强大的深度学习模型。", "本文提出了 \"自组合标签过滤\"，用于有噪音标签的学习，其中标签噪音是与实例无关的，这将产生更准确的不一致预测的识别。", "本文提出了一种从有噪声标签的数据中学习的算法，该算法在更新模型和删除看起来有噪音标签的样本之间交替进行。"]}
+{"source": "Long training times of deep neural networks are a bottleneck in machine learning research.The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth.Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low.However, the choice of which sparse topology should be used in these networks is unclear.In this work, we provide a theoretical foundation for the choice of intra-layer topology.First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks.Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy.To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on.We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.", "target": ["我们研究了在训练前修剪DNN的问题，并对训练先验稀疏网络时应使用哪种拓扑结构提供了答案。", "作者提出用疏散连接的线性层取代密集层，并提出了一种通过测量疏散层对密集层的随机权重的近似程度来寻找最佳拓扑结构的方法。", "本文提出了一种稀疏级联架构，它是几个稀疏矩阵的乘法，也是一种特定的连接模式，其性能优于其他提供的考虑。"]}
+{"source": "Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task.The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices.Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design.NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures.However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures.This procedure needs to be executed from scratch for each new task.The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks.In this paper, we present the Multitask Neural Model Search (MNMS) controller.Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks.We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task.We then show that pre-trained MNMS controllers can transfer learning to new tasks.By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.", "target": ["我们提出了多任务神经模型搜索，这是一个元学习器，可以同时为多个任务设计模型，并将学习转移到未见过的任务。", "本文将神经结构搜索扩展到多任务学习问题上，在这个问题上，学习一个任务条件的模型搜索控制器来同时处理多个任务。", "在本文中，作者总结了他们建立一个框架的工作，称为多任务神经模型搜索控制器，用于同时跨多个任务的自动化神经网络构建。"]}
+{"source": "This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences.We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks.After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames.Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.", "target": ["我们通过生成式深度学习将非线性视觉过程建模为自回归噪音。", "提出了一种新的方法，用深度版的线性过程（马尔可夫过程）来模拟非线性的视觉过程。", "本文提出了一种新的序列深度生成模型，特别是图像序列和视频，它在模型的一部分使用了线性结构。"]}
+{"source": "Partial differential equations (PDEs)  play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc.PDEs are commonly derived based on physical laws or empirical observations.However, the governing equations for many complex systems in modern applications are still not fully known.With the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored.Such vast quantity of data offers new opportunities for data-driven discovery of hidden physical laws.Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models.The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses.A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network.These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory).We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet).Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.", "target": ["本文提出了一个新的前馈网络，称为PDE-Net，用于从数据中学习PDEs。", "本文阐述了使用深度学习机制来识别由PDEs指定的动态系统的目的。", "本文提出了一种基于神经网络的算法，用于从数据中学习，这些数据产生于具有可写成偏微分方程的治理方程的动态系统。", "本文通过使用神经架构的非参数偏微分方程来解决复杂的动力系统建模问题，其中最重要的想法是通过papier（PDE-net）来学习微分算子和支配PDE的函数。"]}
+{"source": "Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism.  However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior.  While it is possible to use normalizing flow approximate posteriors for continuous latents, there is nothing analogous for discrete latents.The most natural approach to model discrete dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow.  We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space models.  To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow.  We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach.  We tested our approach on the neuroscience problem of inferring discrete spiking activity from noisy calcium-imaging data, and found that it gave accurate connectivity estimates in an order of magnitude less time.", "target": ["我们给出了一个类似于正规化流的快速抽样程序，用于离散潜变量模型。", "本文通过使用固定点迭代，将自回归滤波变异近似法用于离散动力系统的参数估计。", "作者设想了一个针对离散变量或其连续松弛的一般自回归后验家族。", "本文有两个主要贡献：它将正规化流程扩展到离散设置���并提出了一个近似的自回归时间序列的定点更新规则，可以利用GPU的并行性。"]}
+{"source": "Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks.However, the success is limited at more knowledge intensive tasks such as QA from a big corpus.Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web.We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size.More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge.We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them (“life-long bAbI”) which has stories of up to 10 million sentences.Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently.Unlike fully differentiable memory models, NGM’s time complexity and answering quality are not affected by the story length.The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992).To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling.To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.", "target": ["我们提出了一个框架，可以学习对知识进行符号化编码，并生成程序对编码的知识进行推理。", "作者提出了N-Gram机器来回答长文档的问题。", "本文介绍了n-gram机器，这是一个将句子编码为简单符号表示的模型，可以有效地进行查询。"]}
+{"source": "We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge.In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions.We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables).We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables).We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs.Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning.Experiments in the two-variable case validate the proposed ideas and theoretical results.", "target": ["本文提出了一个基于对转移分布的适应速度的元学习目标，以发现模块化分解和因果变量。", "本文表明，具有正确基础结构的模型将比具有不正确结构的模型更快地适应因果干预。", "在这项工作中，作者提出了一个通用的、系统的元转移目标框架，其中包含了未知干预下的因果结构学习。"]}
+{"source": "Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially.Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining performance on the previous tasks.We analyze catastrophic forgetting from the perspective of change in classifier likelihood and propose a simple L1 minimization criterion which can be adapted to different use cases.We further investigate two ways to minimize forgetting as quantified by this criterion and propose strategies to achieve finer control over forgetting.Finally, we evaluate our strategies on 3 datasets of varying difficulty and demonstrate improvements over previously known L2 strategies for mitigating catastrophic forgetting.", "target": ["关于灾难性遗忘的另一个视角", "本文介绍了一个对抗灾难性遗忘的框架，该框架基于改变损失项以最小化分类器可能性的变化，通过泰勒序列近似法获得。", "本文试图通过关注正则化方法来解决持续学习的难题，并提出了一种L_1策略来缓解这一问题。"]}
+{"source": "We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attributeediting workflow.Current face modeling methods using 3DMM suffer from the lack of local control.We thus create a 3DMM bycombining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions.Our local PCA-based approachuses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressivewhile allowing accurate reconstruction.The editing controls we provide to the user are intuitive as they are extracted fromanthropometric measurements found in the literature.Out of a large set of possible anthropometric measurements, we filter theones that have meaningful generative power given the face data set.We bind the measurements to the part-based 3DMM throughmapping matrices derived from our data set of facial scans.Our part-based 3DMM is compact yet accurate, and compared toother 3DMM methods, it provides a new trade-off between local and global control.We tested our approach on a data set of 135scans used to derive the 3DMM, plus 19 scans that served for validation.The results show that our part-based 3DMM approachhas excellent generative properties and allows intuitive local control to the user.", "target": ["我们提出了一种构建逼真的三维面部可变形模型（3DMM）的方法，通过选择最佳的特征向量和人体测量集，实现直观的面部属性编辑工作流程。", "提出了人脸网格的分片可变形模型，还提出了人脸的人体测量值和模型参数之间的映射，以便合成和编辑具有所需属性的人脸。", "本文描述了一种基于部件的可变形面部模型的方法，允许区域的用户控制。"]}
+{"source": "We review eight machine learning classification algorithms to analyze Electroencephalographic (EEG) signals in order to distinguish EEG patterns associated with five basic educational tasks.There is a large variety of classifiers being used in this EEG-based Brain-Computer Interface (BCI) field.While previous EEG experiments used several classifiers in the same experiments or reviewed different algorithms on datasets from different experiments, our approach focuses on review eight classifier categories on the same dataset, including linear classifiers, non-linear Bayesian classifiers, nearest neighbour classifiers, ensemble methods, adaptive classifiers, tensor classifiers, transfer learning and deep learning.Besides, we intend to find an approach which can run smoothly on the current mainstream personal computers and smartphones.  The empirical evaluation demonstrated that Random Forest and LSTM (Long Short-Term Memory) outperform other approaches.We used a data set which users were conducting five frequently-conduct learning-related tasks, including reading, writing, and typing.Results showed that these best two algorithms could correctly classify different users with an accuracy increase of  5% to 9%, use each task independently.Within each subject, the tasks could be recognized with an accuracy increase of  4% to 7%, compared with other approaches.This work suggests that Random Forest could be a recommended approach (fast and accurate) for current mainstream hardware, while LSTM has the potential to be the first-choice approach when the mainstream computers and smartphones can process more data in a shorter time.", "target": ["在基于EEG的BCI实验中，两种算法的表现优于其他八种算法"]}
+{"source": "Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems.In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction.We introduce two communication protocols - one grounded in the semantics of the game, and one which is a priori ungrounded.  We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded, cheap talk channel to do the same.  However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge.We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.", "target": ["我们只用强化学习来教代理进行谈判；自私的代理可以这样做，但只能使用值得信赖的沟通渠道，而利社会的代理可以使用随意的谈话进行谈判。", "作者描述了一个谈判游戏的变体，考虑了随意的谈话的二级沟通渠道，发现二级渠道改善了谈判的结果。", "本文探讨了代理人如何学习交流以解决谈判任务，并发现利社会的代理人能够利用RL学习接地符号，但自利的代理人却不能。", "考察了代理人如何在一个简单的谈判游戏中利用沟通来使他们的回报最大化的问题。"]}
+{"source": "The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.  We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.", "target": ["我们提出了一个新颖的元学习框架，用于归纳推理，一次性对整个测试集进行分类以缓解少量数据问题。", "本文提出通过端到端学习标签传播模型的方式来解决少量样本学习的问题，这是第一个学习标签传播的少量样本学习，并产生了有效的经验结果。", "本文提出了一个元学习框架，通过以端到端的方式学习基于图的标签传播，来利用未标记的数据。", "研究在过渡环境下的少数宿主学习：使用元学习来学习将标签从训练样本传播到测试样本。"]}
+{"source": "We describe the use of an automated scheduling system for observation policy design and to schedule operations of the NASA (National Aeronautics and Space Administration) ECOSystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).We describe the adaptation of the Compressed Large-scale Activity Scheduler and Planner (CLASP) scheduling system to the ECOSTRESS scheduling problem, highlighting multiple use cases for automated scheduling and several challenges for the scheduling technology: handling long-term campaigns with changing information, Mass Storage Unit Ring Buffer operations challenges, and orbit uncertainty.The described scheduling system has been used for operations of the ECOSTRESS instrument since its nominal operations start July 2018 and is expected to operate until mission end in Summer 2019.", "target": ["我们描述了使用自动调度系统进行观测策略设计和安排NASA的ECOSTRESS任务的操作。", "本文介绍了对自动调度系统CLASP的调整，以ISS上的一个EO实验（ECOSTRESS）为目标。"]}
+{"source": "Adversarial examples are modified samples that preserve original image structures but deviate classifiers.Researchers have put efforts into developing methods for generating adversarial examples and finding out origins.Past research put much attention on decision boundary changes caused by these methods.This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view.Human beings can learn and classify prototypes as well as transformations of objects.While neural networks store learned knowledge in a more hybrid way of combining all prototypes and transformations as a whole distribution.Hybrid storage may lead to lower distances between different classes so that small modifications can mislead the classifier.A one-step distribution imitation method is designed to imitate distribution of the nearest different class neighbor.Experiments show that simply by imitating distributions from a training set without any knowledge of the classifier can still lead to obvious impacts on classification results from deep networks.It also implies that adversarial examples can be in more forms than small perturbations.Potential ways of alleviating adversarial examples are discussed from the representation point of view.The first path is to change the encoding of data sent to the training step.Training data that are more prototypical can help seize more robust and accurate structural knowledge.The second path requires constructing learning frameworks with improved representations.", "target": ["对所学知识的混合性存储和表示可能是对抗性例子的一个原因。"]}
+{"source": "Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its performance.Although Adam appears to be a natural solution, its performance in AltQ has rarely been studied before.In this paper, we first provide a solid exploration on how well AltQ performs with Adam.We then take a further step to improve the implementation by adopting the technique of parameter restart.More specifically, the proposed algorithms are tested on a batch of Atari 2600 games and exhibit superior performance than the DQN learning method.The convergence rate of the slightly modified version of the proposed algorithms is characterized under the linear function approximation.To the best of our knowledge, this is the first theoretical study on the Adam-type algorithms in Q-learning.", "target": ["基于亚当的Q-Learning的新实验和理论", "本文提供了传统的Q-learning与线性函数近似的收敛结果，当使用类似亚当的更新时。", "本文介绍了一种通过使用亚当优化器和定期重启亚当优化器的内部参数相结合的方法来改进AltQ算法。"]}
+{"source": "In search for more accurate predictive models, we customize capsule networks for the learning to diagnose problem.We also propose Spectral Capsule Networks, a novel variation of capsule networks, that converge faster than capsule network with EM routing.Spectral capsule networks  consist of spatial coincidence filters that detect entities based on the alignment of extracted features on a one-dimensional linear subspace.Experiments on a public benchmark learning to diagnose dataset not only shows the success of capsule networks on this task, but also confirm the faster convergence of the spectral capsule networks.", "target": ["一个新的胶囊网络，在我们的医疗保健基准实验中收敛得更快。", "提出了一种胶囊网络的变体，它不使用EM路由，而是采用由前一个胶囊的加权投票矩阵上的主导特征向量所跨越的线性子空间。", "本文提出了一种改进的路由方法，它采用了eigendecomposition的工具来寻找胶囊的激活和姿势。"]}
+{"source": "One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm.In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data.At the same time, public data can be used for obtaining general knowledge (i.e. general model of English).We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs.We propose a novel technique that significantly improves prediction quality on users’ language compared to a general model and outperforms gradient compression methods in terms of communication efficiency.The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts.Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.", "target": ["我们提出了一种在用户设备上对语言模型进行分布式微调的方法，无需收集私人数据", "本文通过采用用户特定文本和一般英语之间的线性插值目标，在移动设备上改进基于用户输入的一小部分文本的语言模型。"]}
+{"source": "We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudo-posterior.Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be analyzed to provide loss guarantees on the predictions with the pseudo-posterior.Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.", "target": ["本文利用对有界假设空间上的Lipschitz损失的分析，推导出具有强大性能保证的新ERM型算法，可应用于非共轭稀疏GP模型。"]}
+{"source": "In this paper, we propose a novel regularization method, RotationOut, for neural networks. Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector. RotationOut can also be used in convolutional layers and recurrent layers with a small modification.We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction. Using this method, we also show how to use RotationOut/Dropout together with Batch Normalization. Extensive experiments in vision and language tasks are conducted to show the effectiveness of the proposed method. Codes will be available.", "target": ["我们提出了一种神经网络的正则化方法和一种噪音分析方法", "本文提出了一种新的正则化方法来缓解深度神经网络的过拟合问题，即用随机旋转矩阵旋转特征以减少共适应。", "本文提出了一种新的训练神经网络的正则化方法，它以一种相互依赖的方式增加噪音神经��。"]}
+{"source": "Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train.While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup.In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.We model the environment, as seen by each of the agents, using separate but related Markov decision processes.We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference.MA-SAC can be employed in both cooperative and competitive settings.Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios.While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.", "target": ["多代理强化学习的概率框架", "本文在非政策性最大熵行为者批评算法软行为者批评（SAC）的基础上，提出了一种名为多行为者软行为者批评（MA-SAC）的新算法。"]}
+{"source": "Sorting input objects is an important step in many machine learning pipelines.However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization.In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of unimodal row-stochastic matrices, where every row sums to one and has a distinct argmax.This relaxation permits straight-through optimization of any computational graph involve a sorting operation.Further, we use this relaxation to enable gradient-based stochastic optimization over the combinatorially large space of permutations by deriving a reparameterized gradient estimator for the Plackett-Luce family of distributions over permutations.We demonstrate the usefulness of our framework on three tasks that require learning semantic orderings of high-dimensional objects, including a fully differentiable, parameterized extension of the k-nearest neighbors algorithm", "target": ["我们为排序算子提供了一个连续的松弛，实现了端到端的、基于梯度的随机优化。", "本文考虑了如何在不明确一定要学习其实际含义或价值的情况下对一些项目进行排序，并提出了一种通过连续松弛进行优化的方法。", "这项工作建立在sum(top k)特性的基础上，推导出 \"单模行随机 \"矩阵的路径可微调采样器。", "介绍了排序算子的连续松弛，以构建基于端到端的梯度优化，并介绍了其使用Placket-Luce分布和蒙特卡洛的方法的随机扩展。"]}
+{"source": "Transferring knowledge across tasks to improve data-efficiency is one ofthe open key challenges in the area of global optimization algorithms.Readilyavailable algorithms are typically designed to be universal optimizers and, thus,often suboptimal for specific tasks.We propose a novel transfer learning method toobtain customized optimizers within the well-established framework of Bayesianoptimization, allowing our algorithm to utilize the proven generalizationcapabilities of Gaussian processes.Using reinforcement learning to meta-train anacquisition function (AF) on a set of related tasks, the proposed method learns toextract implicit structural information and to exploit it for improved data-efficiency.We present experiments on a sim-to-real transfer task as well as on several simulatedfunctions and two hyperparameter search problems.The results show that ouralgorithm (1) automatically identifies structural properties of objective functionsfrom available source tasks or simulations, (2) performs favourably in settings withboth scarse and abundant source data, and (3) falls back to the performance levelof general AFs if no structure is present.", "target": ["我们通过元学习的神经获取函数，在贝叶斯优化的框架内进行高效灵活的转移学习。", "作者提出了MetaBO，它使用强化学习来元学习贝叶斯优化的获取函数，显示了在新任务上不断提高的样本效率。", "作者提出了一种基于元学习的标准采集函数（AFs）的替代方法，即预训练的神经网络输出采集值作为手选特征的函数。"]}
+{"source": "We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory.The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases.Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X).This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works.To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters.This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations.We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class.Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space.Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering.This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.", "target": ["确定性的深层神经网络不会丢弃信息，但它们会对输入进行分群。", "本文提供了一种原则性的方法来研究深度神经网络中的压缩短语，通过提供一个理论上的听觉熵估计器来估计相互信息。"]}
+{"source": "A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.Our experiments show that the proposed methods lead to significant improvements on cooperative problems.We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.", "target": ["我们提出了促进合作任务协调的多代理 RL算法的正规化目标。", "本文提出了两种使代理人偏向于学习协调行为的方法，并在具有适当复杂性的多代理人领域对这两种方法进行了严格的评估。", "本文在MADDPG的基础上提出了两种方法来鼓励分散的MARL代理之间的协作。"]}
+{"source": "Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities.The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities.However, existing work learns joint representations using multiple modalities as input and may be sensitive to noisy or missing modalities at test time.With the recent success of sequence to sequence models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time.In this paper, we propose a method to learn robust joint representations by translating between modalities.Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities.Once our translation model is trained with paired multimodal data, we only need data from the source modality at test-time for prediction.This ensures that our model remains robust from perturbations or missing target modalities.We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube.Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to perturbations of all other modalities.", "target": ["我们提出了一个模型，该模型通过在多种模式之间进行分层的循环翻译来学习稳定的联合表征。", "本文介绍了多模态循环翻译网络（MCTN��，并对其用于多模态情感分析进行了评估。"]}
+{"source": "The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning.The Hessian is often used to understand these geometric properties.We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian.Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian.We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16.To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature.This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.", "target": ["了解数据生成分布下的神经网络Hessian特征值。", "本文分析了大型神经网络Hessian矩阵的频谱，对最大/最小特征值进行了分析，并使用Lanczos正交方法对频谱进行了视觉化。", "本文利用随机矩阵理论研究了深度学习的经验Hessian和真实Hessian的频谱分布，并提出了一种高效的频谱视觉化方法。"]}
+{"source": "Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text.In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.", "target": ["改善序列模型的一个简单技巧。将它们与一个图模型组合起来", "本文提出了一个结构性总结模型，该模型是由RNN扩展而来的基于图形的编码器。", "这项工作将图谱神经网络与抽象化总结的顺序方法相结合，在所有数据集上与外部基线相比都很有效。"]}
+{"source": "In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability.Nevertheless, it is difficult to determine the number of components.We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM).In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components.The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner.Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity.Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.", "target": ["一个基于判别性高斯混合模型的稀疏分类器，它也可以被嵌入到神经网络中。", "本文提出了一个通过梯度下降论证训练的高斯混合模型，它可以诱导稀疏性并减少可训练的模型层参数。", "本文提出了一个分类器，称为SDGM，基于判别性高斯混合物及其稀疏参数的估计。"]}
+{"source": "We recently observed that convolutional filters initializedfarthest apart from each other using offthe-shelf pre-computed Grassmannian subspacepacking codebooks performed surprisingly wellacross many datasets.Through this short paper,we’d like to disseminate some initial results in thisregard in the hope that we stimulate the curiosityof the deep-learning community towards consideringclassical Grassmannian subspace packingresults as a source of new ideas for more efficientinitialization strategies.", "target": ["使用现成的Grassmannian编码簿初始化权重，获得更快的训练和更好的准确性"]}
+{"source": "Domain adaptation is critical for success in new, unseen environments.Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.  Our model can be applied in a variety of visual recognition and prediction settings.We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.", "target": ["一种无监督的领域适应方法，在像素和特征层面上都能适应。", "本文提出了一种领域适应的方法，通过扩展CycleGAN的任务特定损失函数和对像素和特征施加的损失。", "本文提出了使用CycleGANs进行领域适应的建议", "本文通过将CycleGAN与对抗性适应方法耦合在一起，在CycleGAN的总体目标中包括一个新的特征和语义损失，对以前的工作做了一个新的扩展，有明显的好处。"]}
+{"source": "Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information.The proposed approach efficiently removes affixes from an Amharic word.The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming.While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support.In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix.The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states.The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem.The performance of the generated stemmer is evaluated using manually annotated Amharic words.The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.", "target": ["Amharic Light Stemmer是为提高Amharic Sentiment Classification的性能而设计的。", "本文研究了对形态丰富的语言进行词干提取的问题，采用的是词干提取器，只在保留词中原始语义信息的前提下去除词缀。", "本文提出了一种阿姆哈拉语的轻度词干提取，使用一连串的转换，使形式标准化，去除后缀、前缀和下缀。"]}
+{"source": "Place and grid-cells are known to aid navigation in animals and humans.Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space.We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons.Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks.Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.", "target": ["我们研究了简单的深度网络是否拥有类似网格细胞的人工神经元，同时在学习的概念空间中进行记忆检索。"]}
+{"source": "We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective.Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies.  Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data.The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression.Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.", "target": ["预测性编码角度下基于囊状体的电脑视觉的利与弊", "提出了一个主动视觉问题的计算框架，并解释了如何学习控制策略以减少后验信念的熵值。"]}
+{"source": "Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.To circumvent this problem and learn on graphs, graph feature representation is required.Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.", "target": ["我们从理论上研究了拉普拉斯频谱的一致性，并将其作为全图嵌入的方法。", "本文以图的拉普拉斯频谱为手段，生成一种用于比较图和分类的表示方法。", "这项工作提出使用图拉普拉斯频谱来学习图的表示。"]}
+{"source": "Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD).  In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice.  Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost.  Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy at epsilon=8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at epsilon=2/255 in 12 hours, in comparison to past work based on ``free'' adversarial training which took 10 and 50 hours to reach the same respective thresholds.", "target": ["基于FGSM的对抗性训练，加上随机化，和基于PGD的对抗性训练一样好用：我们可以用它在CIFAR10上训练一个稳定的分类器，只需6分钟，在ImageNet上训练12小时，在单台机器上。", "本文重新审视了随机+FGSM方法，以比以前的方法更快的速度训练针对强PGD逃避攻击的稳健模型。", "本文的主要主张是，随机化加快速梯度符号法（FGSM）对抗性训练的简单策略可以产生稳定的神经网络。"]}
+{"source": "In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training.The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values.But it cannot provide useful gradients and therefore requires complex optimization techniques.The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent.Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity.Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant.Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level.We also show that DeepHoyer can be applied to both element-wise and structural pruning.", "target": ["我们为DNN的修剪提出了几乎无处不在的可微分和尺度不变的正则器，它可以通过标准的SGD训练来实现超值的稀疏性。", "本文提出了一个受Hoyer度量启发的尺度不变量正则器（DeepHoyer），以强制执行神经网络的稀疏性。"]}
+{"source": "Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data.However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise).In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help.We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.We call this approach limited self-supervision, as we limit ourselves to only the data at-hand.We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data.Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task.Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains.In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62).Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.", "target": ["我们表明，自监督的辅助任务要想对时间序列分类有用，并不需要额外的未标记数据，并提出了新的有效的辅助任务。", "本文提出了一种自监督的方法，通过设计基于数据内部结构的辅助任务来创建更多的标签化辅助训练任务，从而从医疗环境中的时间序列数据中学习。", "本文提出了一种对时间序列进行自我监督学习的方法。"]}
+{"source": "Are neural networks biased toward simple functions?Does depth always help learn more complex features?Is training the last layer of a network as good as training all layers?These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.Roughly, the CK and the NTK tell us respectively ``\"what a network looks like at initialization\" and \"``what a network looks like during and after training.\"Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.", "target": ["共轭（又称NNGP）和神经正切核的特征值可以在布林立方体上以封闭形式计算，并揭示了超参数对神经网络归纳偏差、训练和概括的影响。", "本文对神经网络的共轭核和布林立方体上的神经正切核进行了频谱分析，以解决为什么深度网络偏向于简单函数的问题。"]}
+{"source": "To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain.Here we consider atlases used to parcellate the brain when studying brain function.We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.", "target": ["所有的大脑功能分区都是错误的，但有些是有用的"]}
+{"source": "High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.  In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?We show that adversarial imitation can work well even in this high dimensional observation space.Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.  Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.", "target": ["使用非政策性RL和一个微小的对抗性学习的奖励函数，从像素进行模仿，含稀少奖励或没有奖励。", "本文提出在高维视觉空间下的生成式对抗性模仿学习中使用 \"最小对抗者\"。", "本文旨在解决在高维输入环境下���计稀疏奖励的问题。"]}
+{"source": "In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.We provide results on MNIST, CIFAR10, music and speech data.", "target": ["我们展示了轻松识别用生成式对抗网络框架生成的虚假样本的策略。", "表明用普通生成对抗网络（GAN）实现的假样本很容易用各种统计技术来识别。", "本文提出了基于简单的边际统计或从真实数据中自动生成的形式规范的统计数据来识别使用GANs生成的假数据。"]}
+{"source": "Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.", "target": ["我们提出了一个分析框架，以确定所有三个深度学习训练GEMM中的累积位宽要求，并通过基准实验验证我们方法的有效性和严密性。", "作者提出了一种分析方法来预测卷积层和全连接层的部分求和所需的尾数位元数", "作者对神经网络训练中的累加操作所需的数字精度进行了深入分析，并展示了减少浮点累加器中的位元数所带来的理论影响。"]}
+{"source": "Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space.This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping representations for the two domains well-separated.Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one.To handle both within and cross domain verifications, we propose a Feature Transfer Network (FTN) to separate the target feature space from the original source space while aligned with a transformed source space.Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain.In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.", "target": ["一种新的非监督领域适应距离度量学习的理论，并将其应用于不同民族差异的人脸识别。", "提出了一种新型的特征转移网络，优化了领域对抗性损失和领域分离性损失。"]}
+{"source": "In this paper, we consider the problem of training neural networks (NN).To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.", "target": ["我们提出了一种收敛的近似型随机梯度下降算法，用于解决有约束的非光滑非凸面优化问题", "本文提出了Prox-SGD，这是一个随机优化算法的理论框架，显示出对于平滑的非凸面损失+凸面约束/正则器来说，渐进地收敛到静止性。", "本文通过将近似算法的理论适应于非凸设置，提出了一种新的基于梯度的随机优化算法，具有梯度平均性。"]}
+{"source": "The loss of a few neurons in a brain rarely results in any visible loss of function.However, the insight into what “few” means in this context is unclear.How many random neuron failures will it take to lead to a visible loss of function?In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces.We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.We give provable guarantees on the robustness of the network to these crashes.Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar.The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks.We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture.We design an algorithm achieving fault tolerance using a reasonable number of neurons.In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.", "target": ["我们给出了一个NN在随机权重失效情况下的输出误差的约束，在附近神经元相似的连续极限中使用了泰勒扩展", "本文考虑了从神经网络中丢弃神经元的问题，表明如果目标是在评估过程中对随机丢弃的神经元变得稳定，那么只需进行随机失活训练即可。", "这一贡献研究了随机神经元的删除对训练过的架构的预测准确性的影响，并应用于故障分析和神经形态硬件的具体环境。"]}
+{"source": "Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound.This is primarily due to lack of data that captures the interplay of action and sound.In this work, we perform the first large-scale study of the interactions between sound and robotic action.To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.Using this data, we explore the synergies between sound and action, and present three key insights.First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object.Finally, object representations derived from audio embeddings are indicative of implicit physical properties.We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.", "target": ["我们探索和研究声音和行动之间的协同作用。", "本文通过用倾斜机器人建立一个声音-动作-视觉数据集来探索动作和声音之间的联系。", "本文研究音频在物体和动作感知中的作用，以及听觉信息如何帮助学习前向和反向动力学模型。"]}
+{"source": "Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation.Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels.Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT).In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly.We conduct our method on both image classification and semantic segmentation.Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context.Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.", "target": ["我们提出了分层互补目标训练，这是一种新的训练范例，可以有效地利用标签空间中的类别层次来进行图像分类和语义分割。", "一种使类的后验分布的熵正规化的方法，可用于图像分类和分割任务"]}
+{"source": "There is a growing interest in automated neural architecture search (NAS).To improve the efficiency of NAS, previous approaches adopt  weight sharing method to force all models share the same set of weights.  However, it has been observed that a model performing better with shared weights does not necessarily perform  better when trained alone.In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.To alleviate this problem, we present a practical approach to guide the parameter posterior towards its true distribution.Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved.The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet.In our small search space, our model PC-NAS-S attains76.8% top-1 accuracy, 2.1% higher than MobileNetV2 (1.4x) with the same latency.When adopted to our large search space, PC-NAS-L achieves 78.1% top-1 accuracy within 11ms.The discovered architecture also transfers well to other computer vision applications such as object detection and person re-identification.", "target": ["我们的论文确定了现有的神经结构搜索中的权重共享方法的问题，并提出了一个实用的方法，取得了强大的效果。", "作者发现了NAS的一个问题，称为后验衰减，并介绍了后验收敛NAS以减轻这种影响。"]}
+{"source": "Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.In this paper, we claim that such overfitting can be avoided by \"early stopping\" training a deep neural network before the noisy labels are severely memorized.Then, we resume training the early stopped network using a \"maximal safe set,\" which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4–8.2 percent points under existence of real-world noise.", "target": ["我们提出了一种基于 \"早期停止 \"的新颖的两阶段训练方法，用于在噪音标签上进行稳定的训练。", "论文提出研究优化中的早期停止如何帮助找到有把握的例子", "本文提出了一种两阶段的训练方法，用于带标签噪音的学习。"]}
+{"source": "Learning when to communicate and doing that effectively is essential in multi-agent tasks.Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.", "target": ["我们介绍了IC3Net，它是一个单一的网络，可以用来训练合作、竞争和混合场景下的代理。我们还表明，代理可以使用我们的模型学习何时进行交流。", "作者提出了一种新的多代理强化学习架构，该架构使用几个权重相同的LSTM控制器，相互传输一个连续向量。", "���者提出了一个有趣的门控方案，允许代理在多代理RL环境中进行通信。"]}
+{"source": "Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text.Despite the advances in abstractive summarization, custom generation of summaries (e.g. towards a user's preference) remains unexplored.In this paper, we present CATS, an abstractive neural summarization model, that summarizes content in a sequence-to-sequence fashion but also introduces a new mechanism to control the underlying latent topic distribution of the produced summaries.Our experimental results on the well-known CNN/DailyMail dataset show that our model achieves state-of-the-art performance.", "target": ["我们提出了第一个能够定制生成的摘要的神经抽象化模型。"]}
+{"source": "We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameters (handled by a signal compression function), so that the framework can be easily extended to handle different combinations of neural net and compression type.In addition, it has other advantages, such as easy integration with deep learning frameworks, efficient training time, competitive practical performance in the loss-compression tradeoff, and reasonable convergence guarantees.Our toolkit is written in Python and Pytorch and we plan to make it available by the workshop time, and eventually open it for contributions from the community.", "target": ["我们提出了一个基于学习-压缩算法思想的软件框架，它允许人们通过不同的压缩机制（剪枝、量化、低等级等）来压缩任何神经网络。", "本文介绍了一个软件库的设计，通过隐藏压缩方法的细节，使用户更容易压缩他们的网络。"]}
+{"source": "This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.First, the inference networks are trained to match the speaker identity between the two different modalities.Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.", "target": ["本文提出了一种基于自监督学习框架的端到端多模态人脸生成方法。", "本文提出了一个多模态学习框架，它将推理阶段和生成阶段连结起来，以寻求仅从语音中生成人脸的可能性。", "这项工作的目的是建立一个来自音频信号的有条件的人脸图像生成框架。"]}
+{"source": "We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.The results of this network are then processed by two recurrent neural networks.One of the interesting aspects of our model is how propositional atoms are treated.For example, the model is insensitive to their names, it only matters whether they are the same or distinct.", "target": ["提出了一种自上而下的方法，即如何用神经网络循环地表示命题公式。", "本文提供了一个新的逻辑公式的神经网络模型，它通过自上而下地遍历解析树来收集关于一个给定公式的信息。", "本文追求的是与命题微积分公式的解析树同构的树状结构网络的路径，但通过自上而下而不是自下而上地传递信息。"]}
+{"source": "Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.We identify three key challenges that any algorithm needs to master in order to perform well on all games:  processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.  In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of 0.999 (instead of 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.When tested on a set of 42 Atari games, our algorithm exceeds the performance  of an average human on 40 games using a common set of hyper parameters.", "target": ["Ape-X DQfD = 分布式（许多演员+一个学习者+优先重放）DQN，展示优化了雅达利上未裁减的0.999折的回报。", "本文提出了对DQN的三个扩展（贝尔曼更新、时间一致性损失和专家论证），以提高在Atari游戏上的学习性能，在Atari游戏上取得了超过最先进的结果。", "本文提出了一个转化的贝尔曼运算子，旨在解决对未裁减奖励的敏感性、对贴现因子值的稳定性以及探索问题。"]}
+{"source": "The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.%The resulting model encodes relations that better generalize across instances.In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.", "target": ["在有监督的训练过程中对所学的嵌入进行严格约束的训练方法。应用于视觉问题回答。", "作者提出了一个框架，将额外的语义先验知识纳入深度学习模型的传统训练中，使嵌入空间而不是参数空间正规化。", "本文主张在多模态神经网络的语言嵌入层中对外部知识进行编码，作为一组硬约束。"]}
+{"source": "Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.To combine these two concepts, we present a new kind of neural networks—algorithmic neural networks (AlgoNets).These networks integrate smooth versions of classic algorithms into the topology of neural networks.Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.", "target": ["通过使用平滑近似的前向算法来训练逆向模型来解决逆向问题。"]}
+{"source": "Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.Typical convolutional attentions maps are prune to high false positive regions.To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.A public reproducible PyTorch implementation is provided.", "target": ["一种用于弱监督的点状定位的深度学习方法，只使用图像级标签进行学习。它依靠条件熵来定位相关和不相关的区域，旨在最小化假阳性区域。", "这项工作利用新颖的正则化项设计和递归擦除算法，探索了WSL的问题。", "本文提出了一种新的弱监督方法，用于学习带有图像级别标签的物体分割。"]}
+{"source": "Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency.Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model.One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences.Search-control is critical to improve learning efficiency.In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function.Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct.We empirically show that a high frequency function is more difficult to approximate.This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples.We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach.We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.", "target": ["从高频区域获取状态，用于Dyna的搜索控制。", "作者提议在高频域做采样，以提高采样效率", "本文提出了一种在dyna算法中选择做转换的状态的新方法。"]}
+{"source": "We propose a new architecture for distributed image compression from a group of distributed data sources.The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy.The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources.Its compression capability is much better than the method of training codecs separately.Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources.We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC).Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources.Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level.To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.", "target": ["我们介绍了一个基于分布式循环自动编码器的数据导向的分布式源编码框架，用于可扩展的图像压缩（DRASIC）。", "该文提出了一种用于图像压缩的分布式循环自动编码器，它使用ConvLSTM来学习二进制代码，这些代码是由先前编码信息的残余部分逐步构建的。", "作者提出了一种在多个源上训练图像压缩模型的方法，每个源上有一个单独的编码器，还有一个共享的解码器。"]}
+{"source": "Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.", "target": ["门在LSTM中通过计算元素加权和来完成所有繁重的工作，去除内部的简单RNN并不会降低模型的性能。", "本文通过消除内容项和输出门的非线性，提出了一种简化的LSTM变体。", "本文对LSTMS进行了分析，表明它们有一种形式，即每一步的存储单元内容是每一步计算的 \"内容更新 \"值的加权组合，并对LSTM进行了简化，即在每一步计算存储单元的值是输入的确定性函数，而不是输入和当前环境的函数。", "本文对LSTM提出了新的见解，其中核心是一个元素加权和，并认为LSTM只保留输入门和遗忘门来计算权重是多余的。"]}
+{"source": "Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.  Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.", "target": ["通过分析近期机器学习会议上的300多篇论文，我们发现，在可重复性指标方面，机器学习促进健康（ML4H）的应用落后于其他机器学习领域。", "本文对ML医疗应用的可重制性状况进行了定量和定性的审查，并提出了使研究更可重制的建议。"]}
+{"source": "We propose a solution for evaluation of mathematical expression.However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.More difficult or complex task can then be solved using a combination of these smaller network.In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc).These fundamental operations are then learned using simple feed forward neural networks.We then shows that different operations can be designed simply by reusing these smaller networks.As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.", "target": ["我们训练许多小的网络，每个网络都针对一个特定的操作，然后将这些网络组合起来，以执行复杂的操作。", "本文提出使用神经网络来评估数学表达式，通过设计8个基本运算的小积木，如加法、减法等，然后用这些小积木设计多位数的乘法和除法。", "本文提出了一种设计基于NN的数学表达评估引擎的方法。"]}
+{"source": "In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.The generator is trained to increase the probability that fake data is real.We argue that it should also simultaneously decrease the probability that real data is real because1) this would account for a priori knowledge that half of the data in the mini-batch is fake,2) this would be observed with divergence minimization, and3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).We show that IPM-based GANs are a subset of RGANs which use the identity function. Empirically, we observe that1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.", "target": ["使用相对论判别器提高GANs的质量和稳定性；IPM GANs（如WGAN-GP）是一个特例。", "本文提出了一个 \"相对论判别器\"，虽然对超参数、架构和数据集有点敏感，但在某些情况下有帮助。", "在这项工作中，作者考虑了GAN的一个变化，即同时降低生成器的真实数据的概率。"]}
+{"source": "Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.", "target": ["一个基于状态值函数的MPO版本，在离散和连续控制的广泛任务中取得了良好的效果。", "本文提出了一种政策性强化学习的算法，该算法可以处理连续/离散控制、单/多任务学习，并同时使用低维状态和像素。", "本文提出了MPO的一个在线变体--V-MPO，它可以学习V型函数，并朝着优势更新非参数分布。"]}
+{"source": "Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.We study this problem through the lens of fundamental computer science problems: sorting and graph processing.We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.", "target": ["我们提出了神经执行引擎（NEEs），它利用学习到的掩码和受监督的执行痕迹来模仿子程序的功能，并表现出强大的泛化能力。", "本文研究了一个用神经网络构建程序执行引擎的问题，提出了一个基于变压器的模型来学习基本的子程序，并将其应用于几个标准算法中。", "本文讨论的是设计能够学习和实现一般程序的神经网络架构的问题。"]}
+{"source": "Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.", "target": ["贝叶斯变化点检测能够直接从时间序列数据中进行元学习。", "本文考虑了在任务非分割设置中的元学习，并应用贝叶斯在线变化点检测与元学习。", "本文将元学习推向任务不分割的环境，其中MOCA框架采用贝叶斯变化点估计方案进行任务变化检测。"]}
+{"source": "People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.Fricative phonemes have an important part of their content concentrated in high frequency bands.It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.", "target": ["基于深度学习的零延迟摩擦音检测方法", "本文采用有监督的深度学习方法来检测摩擦音音素的确切持续时间，以改进实际的降频算法。"]}
+{"source": "Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.", "target": ["一种在线和线性时间的注意力机制，在输入序列中自适应定位的块上执行软性注意力。", "本文对[1]中的单调注意力提出了一个小的修改，即在单调注意力所预测的片段上增加一个软性注意。", "本文提出了对以前的单调注意模型的扩展（Raffel等人，2017），以关注一个固定大小的窗口，直到对齐位置。"]}
+{"source": "We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network.An image patch is a group of pixels residing in a continuous area contained in the sample.Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training.In addition, we conduct systematic experiments and provide evidence that CNN’s generalization capabilities do not correlate with human recognizable features present in training samples.We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance.Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.", "target": ["开发依靠补丁重排的新技术，以便能够详细分析数据集与训练和概括性能的关系。"]}
+{"source": "Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning.One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations.However, domain randomization is highly inefficient and may lead to policies with high variance across domains.In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant.We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.", "target": ["我们使用一种新的正则化技术，产生了对广泛环境具有良好概括性的强化学习代理。", "本文介绍了强化学习领域随机化中的高变异策略挑战，并主要关注视觉化随机化问题，不同的随机化领域仅在状态空间上存在差异，而基础奖励和动力学是相同的。", "为了提高深度RL代理在不同视觉模式的任务中的泛化能力，本文提出了一种简单的正则化技术，用于领域随机化。"]}
+{"source": "Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest.The field of deep neural networks has mostly left inspiration from these claims out.In this paper, we propose three architectures and use each of them to explore the intersection of network neuroscience and deep learning in an attempt to bridge the gap between the two fields.Using the teachings from network neuroscience and connectomics, we show improvements over the ResNet architecture, we show a possible connection between early training and the spectral properties of the network, and we show the trainability of a DNN based on the neuronal network of C.Elegans.", "target": ["我们探索网络神经科学和深度学习的交叉点。"]}
+{"source": "Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.  In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.  Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.We demonstrate this by constructing a high precision (typically 97\\%+) knowledge base for people from a single seed fact.", "target": ["本文提出了一个非监督的、高精度的知识库构建系统，使用概率程序来定义将知识库事实转换为非结构化文本的过程。", "关于现有知识库的概述，该知识库是用概率模型构建的，与其他知识库建构方法YAGO2、NELL、Knowledge Vault和DeepDive进行了评估。", "本文使用一个描述实体的事实可以在文本和大量网页中实现的概率程序，来学习使用单一的种子事实进行关于人的事实提取。"]}
+{"source": "Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations.Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms.Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method.We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.", "target": ["傅里叶域中的新信号提取方法"]}
+{"source": "We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.The network’s ability to capture interaction dynamics in swarms is demonstrated through transfer learning.We finally discuss the inherent availability and challenges in the scalability of GNN, and proposed a method to improve it with layer-wise tuning and mixing of data enabled by padding.", "target": ["提高图形神经网络在模仿学习和预测蜂群运动上的可扩展性", "本文提出了一个新的时间序列模型，用于学习一连串的图形。", "这项工作考虑了多代理系统中的序列预测问题。"]}
+{"source": "Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints.In this work, we propose a generic and end-to-end learnable compression framework termed differentiable product quantization (DPQ).We present two instantiations of DPQ that leverage different approximation techniques to enable differentiability in end-to-end learning.Our method can readily serve as a drop-in alternative for any existing embedding layer.Empirically, DPQ offers significant compression ratios (14-238x) at negligible or no performance cost on 10 datasets across three different language tasks.", "target": ["我们提出了一个可微分的产品量化框架，可以在没有性能成本的情况下减少端到端训练中的嵌入层的大小。", "本文致力于研究压缩嵌入层以实现低内存推理的方法，其中压缩嵌入是以可微分的端到端方式与特定任务模型一起学习。"]}
+{"source": "For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.We empirically demonstrate on several synthetic problems that our method(i) can learn multi-valued functions and produce the conditional modes,(ii) scales well to high-dimensional inputs and(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.", "target": ["我们介绍了一种简单而新颖的模态回归算法，该算法易于扩展到大型问题。", "本文提出了一种隐含函数的方法来学习多模态回归的模式。", "本工作提出了一种参数化的方法，利用多模分布的隐含函数定理估计条件模式。"]}
+{"source": "Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.Current methods rely heavily on on-policy experience, limiting their sample efficiency.They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.This probabilistic interpretation enables posterior sampling for structured and efficient exploration.We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.", "target": ["将概率任务变量的变异推理与非政策性RL相结合，实现高效的样本meta-RL", "本文提出在元训练时间内使用非政策性RL，极大地提高Meta-RL方法的采样效率。"]}
+{"source": "Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications.However, existing knowledge bases contain very little data compared to the wealth of information on the Web.This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from.Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall.As a result, these large-scale extractions remain unexploited.In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes.MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base.We make three major contributions.First, we introduce a novel concept, web source slices, to describe the contents of a web source.Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base.Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices.We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.", "target": ["本文的重点是为工业知识库扩增管道确定高质量的网络资源。"]}
+{"source": "We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.Challenges arise in practice.As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.Worse yet, we have no prior knowledge on the underlying model for a given scenario.These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances.To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common.This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand.Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms.It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets.Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.", "target": ["我们研究了在匹配预测问题上采用神经网络的优点，在这个问题上，人们试图根据部分组别比较数据来估计一组M项比另一组更受欢迎的可能性。", "本文提出了一种深度神经网络对集合排名问题的解决方案，并为这项任务设计了一个架构，其灵感来自于以前的人工设计算法。", "本文提供了一种使用深度学习架构来解决匹配预测问题的技术。"]}
+{"source": "Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.  Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs).  We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices.This is done by parametrizing with a skew-symmetric matrix using the Cayley transform.Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones.  The proposed training scheme involves a straightforward gradient calculation and update step.In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.", "target": ["在RNN中保持正交循环权重矩阵的一种新方法。", "介绍了一种在神经网络中学习循环参数矩阵的方案，该方案使用Cayley变换和缩放权重矩阵。", "本文提出了一种RNN的重新参数化，即用Cayley转换的偏斜对称矩阵来保持循环权重矩阵的正交性。", "新的RNN参数化允许相对容易地表示正交权重矩阵。"]}
+{"source": "A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language.These seemingly very different tasks are usually solved by specially designed architectures.In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks.We perform extensive experiments to test this insight on 10 disparate tasks as broad as dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving comparable performance as state-of-the-art specialized models.We further demonstrate benefits in multi-task learning.We convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.", "target": ["我们使用一个单一的模型来解决大量的自然语言分析任务，以统一的跨度关系格式来制定这些任务。", "本文将广泛的自然语言处理任务概括为一个基于跨度的框架，并提出了一个解决所有这些问题的通用架构。", "这项工作提出了各种短语和标记级NLP任务的统一表述。"]}
+{"source": "Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models.With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results.We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.Our bound instead directly parameterises a free matrix, which is an additional variational parameter.At the local maxima of the bound, this matrix is equal to the matrix inverse.We prove that our bound gives the same guarantees as earlier variational approximations.We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.", "target": ["我们提出了一个GP模型的变异下限，可以在不计算昂贵的矩阵运算（如求逆）的情况下进行优化，同时提供与现有变异近似值相同的保证。"]}
+{"source": "It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations.Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature.While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models.We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned.This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.", "target": ["以恒定曲率黎曼流形的产物为模型的潜空间的变异自动编码器在图像重建方面比单一流形的变异自动编码器有所改善。", "本文介绍了VAE概念的一般表述，其潜在空间由弯曲流形组成。", "本文是关于在非欧几里得空间发展VAE。"]}
+{"source": "Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery.We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties.Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step.We show that this finding is invariant to the choice of translation model, making this a \"black box\" algorithm.We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.This simple, powerful technique operates strictly on the inputs and outputs of any translation model.We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models.Our method provides a significant boost in performance relative to its non-recursive peers with just a simple \"``for\" loop.Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.", "target": ["我们介绍了一种使用翻译框架对化合物进行反复优化的黑箱算法。", "作者将分子优化框定为一个序列到序列的问题，并扩展了现有的改进分子的方法，表明它有利于优化logP，但不利于优化QED。", "本文在为分子优化开发的现有翻译模型的基础上，对序列到序列或图到图的翻译模型进行了迭代使用。"]}
+{"source": "Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance.The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application.A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement.We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario.BlackMarks takes the pre-trained unmarked model and the owner’s binary signature as inputs.The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark.To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit ‘0’ and bit ‘1’.Given the owner’s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks.The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss.To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.We perform a comprehensive evaluation of BlackMarks’ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness.BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.", "target": ["提出了第一个利用DNN的输出进行多位元签名嵌入和提取的水印框架。", "提出了一种在黑箱环境下对神经网络进行多位元水印的方法，并证明现有模型的预测可以携带一个多位元字符串，以后可以用来验证所有权。", "本文提出了一种模型水印的方法，其中水印是作为微调程序的一部分嵌入到模型中的一个位元字符串。"]}
+{"source": "Adversarial training provides a principled approach for training robust neural networks.From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem.The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples.Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure.This work proposes a new adversarial training method based on a general learning-to-learn framework.Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network.At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer.From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.", "target": ["不知道如何优化？那就学着去优化吧!", "本文提出了一种训练图像分类模型以抵抗L-无穷大扰动攻击的方法。", "本文提出使用学习到学习的框架来学习一个攻击者。"]}
+{"source": "In this work we introduce a new framework for performing temporal predictionsin the presence of uncertainty.It is based on a simple idea of disentangling com-ponents of the future state which are predictable from those which are inherentlyunpredictable, and encoding the unpredictable components into a low-dimensionallatent variable which is fed into the forward model.Our method uses a simple su-pervised training objective which is fast and easy to train.We evaluate it in thecontext of video prediction on multiple datasets and show that it is able to consi-tently generate diverse predictions without the need for alternating minimizationover a latent space or adversarial training.", "target": ["一种简单易行的时间序列多模态预测的训练方法。", "本文介绍了一个时间序列预测模型，该模型学习了一个确定性的映射，并训练另一个网络来预测未来的帧，给定的输入和第一个网络的剩余误差。", "本文提出了一个不确定条件下的预测模型，他们把确定性的组件预测和不确定性的组件预测分开。"]}
+{"source": "Conducting reinforcement-learning experiments can be a complex and timely process.A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof.In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments.This paper gives an overview  of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.", "target": ["本文介绍了simple_rl，这是一个新的开源库，用于在Python 2和3中进行强化学习实验，重点是简单性。"]}
+{"source": "Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint.In this study, we prove the local stability of optimizing the simple gradient penalty $\\mu$-WGAN(SGP $\\mu$-WGAN) under suitable assumptions regarding the equilibrium and penalty measure $\\mu$.The measure valued differentiation concept is employed to deal with the derivative of the penalty terms, which is helpful for handling abstract singular measures with lower dimensional support.Based on this analysis, we claim that penalizing the data manifold or sample manifold is the key to regularizing the original WGAN with a gradient penalty.Experimental results obtained with unintuitive penalty measures that satisfy our assumptions are also provided to support our theoretical results.", "target": ["本文通过引入量值微分的概念来处理简单梯度惩罚$mu$-WGAN优化的稳定性。", "研究了具有平方的零中心梯度惩罚项的WGAN对一个一般措施的影响。", "描述了梯度惩罚的Wasserstein GAN的收敛性。"]}
+{"source": "We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values.Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation.  We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks.We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.", "target": ["基于随机松弛权重分区的交替优化的二元和三元权重网络的最先进训练方法", "本文提出了一种优化三元神经网络的新训练方案。", "作者提出了RPR，一种随机分配和量化权重并训练剩余参数的方法，然后在交替循环中进行放松，以训练量化的模型。"]}
+{"source": "Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs).Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy.Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences.In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks.This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.", "target": ["我们用一个辅助损失来代替分层RNN中的一些梯度路径。我们表明，这可以减少内存成本，同时保持性能。", "本文介绍了一种分层的RNN架构，可以训练更有效地内存。", "本文建议使用辅助损失来解开RNN中不同层次的结构。"]}
+{"source": "In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations.In particular, we use singular value decomposition to analyze the relevant variations in feature space.These variations are formalized as the effective dimension of the embedding.We consider how the effective dimension varies across layers within class.We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation.Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.", "target": ["做好分类工作的神经网络在将点压缩到较少的维度之前，会将其投影成更多的球形。"]}
+{"source": "Deep learning methods have achieved high performance in sound recognition tasks.Deciding how to feed the training data is important for further performance improvement.We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.We then input the mixed sound to the model and train the model to output the mixing ratio.The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.As a result, we achieved a performance surpasses the human level.", "target": ["我们提出了一种新的深度声音识别的学习方法，名为BC学习。", "作者定义了一个新的学习任务，要求DNN预测两个不同类别的声音之间的混合比例，以提高最终学习网络的辨别能力。", "提出了一种通过生成 \"类间 \"训练样本来提高通用学习方法性能的方法，并介绍了所提出技术的基本直觉和必要性。"]}
+{"source": "Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable.Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results.The problem could be exacerbated in black-box prediction models, such as deep neural networks.In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels.Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures.We evaluate our proposed method on forecasting temperatures in Los Angeles.", "target": ["我们提出了一种方法，为时空预测推断出随时间变化的数据质量水平，而无需明确指定标签。", "引入了一个新的数据质量定义，它依赖于（Zhou和Scholkopf）中定义的区域变化的概念，并将其扩展到多个异质数据源。", "这项工作提出了一种用时变图模型评估不同数据源质量的新方法，质量水平作为目标函数中的正则化项。"]}
+{"source": "Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.", "target": ["我们提出了3D形状程序，这是一种结构化的、组合式的形状表示。我们的模型可以学习推论和执行形状程序来解释三维形状。", "一种推断给定3D模型的形状程序的方法，其结构包括一个对3D形状进行编码并输出指令的循环网络，以及一个将程序变成3D的第二模块。", "本文介绍了一个由ShapeProgram给出的3D形状的高阶语义描述。"]}
+{"source": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention  thanks to its encouraging performance on a variety of control tasks.Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment.In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult.We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better.Our findings are further confirmed to be robust against the choice of training hyperparameters.We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough.We hope our study provides guidance for future practices in regularizing policy optimization algorithms.", "target": ["我们表明，传统的正则化方法（如$L_2$，随机失活），在RL方法中基本上被忽略了，但在策略优化中可以非常有效。", "作者研究了强化学习领域现有的一套直接策略优化方法，并详细调查了法规对遵循这些方法的代理人的性能和行为的影响。", "本文提供了一个关于正则化对训练环境中多个连续控制任务的策略优化方法的性能影响的研究。"]}
+{"source": "We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images.The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection.To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives.In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements.We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline.Preliminary results indicate that the task poses a significant machine learning challenge.We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.", "target": ["我们提出了一个回答问题的数据集FigureQA，作为开发能够从数据的视觉表现中直观地识别模式的模型的第一步。", "本文介绍了一个关于数字的模板化问题回答的数据集，涉及对数字元素的推理。", "本文介绍了一个名为Figure-QA的新的视觉推理数据集，该数据集由14万张图象和1.55万个QA对组成，可以帮助开发能够从数据的视觉表示中提取有用信息的模型。"]}
+{"source": "In this paper, I discuss some varieties of explanation that can arisein intelligent agents.I distinguish between process accounts, whichaddress the detailed decisions made during heuristic search, andpreference accounts, which clarify the ordering of alternativesindependent of how they were generated.I also hypothesize which types of users will appreciate which types of explanation.In addition, I discuss three facets of multi-step decision making-- conceptual inference, plan generation, and plan execution --in which explanations can arise.I also consider alternative waysto present questions to agents and for them provide their answers.", "target": ["这份立场文件分析了规划和相关系统中可能出现的不同类型的自我解释。", "讨论了解释的不同方面，特别是在顺序决策的背景下。"]}
+{"source": "Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details.Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery.To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion:(i) co-registration,(ii) fusion,(iii) up-sampling, and(iv) registration-at-the-loss.Co-registration of low-res views is learned implicitly through a reference-frame channel, with no explicit registration mechanism.We learn a global fusion operator that is applied recursively on an arbitrary number of low-res pairs.We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet.We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth observation data at scale.Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.", "target": ["第一个深度学习的MFSR方法，以端到端的方式解决注册、融合、上采样的问题。", "本文提出了一种端到端的多帧超级解析度算法，该算法依赖于一个成对的共同注册和融合块（卷积剩余块），嵌入到一个估计超级解析度图像的编码器-解码器网络 \"HighRes-net\"。", "本文提出了一个包括递归融合到共同注册损失的框架，以解决超解析度结果和高分辨率标签没有像素对齐的问题。"]}
+{"source": "Large mini-batch parallel SGD is commonly used for distributed training of deep networks.Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication.In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.SGP uses a gossip algorithm called PushSum for approximate distributed averaging, allowing for much more loosely coupled communications which can be beneficial in high-latency or high-variability scenarios.The tradeoff is that approximate distributed averaging injects additional noise in the gradient which can affect the train and test accuracies.We prove that SGP converges to a stationary point of smooth, non-convex objective functions.Furthermore, we validate empirically the potential of SGP.For example, using 32 nodes with 8 GPUs per node to train ResNet-50 on ImageNet, where nodes communicate over 10Gbps Ethernet, SGP completes 90 epochs in around 1.5 hours while AllReduce SGD takes over 5 hours, and the top-1 validation accuracy of SGP remains within 1.2% of that obtained using AllReduce SGD.", "target": ["对于高延迟网络上的分布式训练，使用基于流言的近似分布式平均法，而不是像AllReduce那样的精确分布式平均法。", "作者提出��用流言算法作为计算一组工人的近似平均数的一般方法", "本文证明了SGP对于非凸平滑函数的收敛性，并表明SGP在低延迟环境下可以实现显着的速度提升，而不会牺牲太多的预测性能。"]}
+{"source": "In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on.The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator.We also explore two approaches to accomplish the conditional discriminator: (1) $phredGAN_a$, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) $phredGAN_d$, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance.To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends.Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation.We also explore the trade-offs from using either variant of $phredGAN$ on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).", "target": ["本文开发了一个对抗性学习框架，用于具有角色的神经对话模型。", "本文提出了对hredGAN的扩展，以同时学习一组代表每个说话人的角色的属性嵌入，并生成基于角色的反应。"]}
+{"source": "We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space.Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task.This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.", "target": ["生物启发的人工神经网络，由定位在二维空间的神经元组成，能够形成独立的群体，执行不同的任务。"]}
+{"source": "The transformer has become a central model for many NLP tasks from translation to language modeling to representation learning.Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks.In theory attention also offers more insights into the model’s internal decisions; however, in practice when stacked it quickly becomes nearly as fully-connected as recurrent models.In this work, we propose an alternative transformer architecture, discrete transformer, with the goal of better separating out internal model decisions.The model uses hard attention to ensure that each step only depends on a fixed context.Additionally, the model uses a separate “syntactic” controller to separate out network structure from decision making.Finally we show that this approach can be further sparsified with direct regularization.Empirically, this approach is able to maintain the same level of performance on several datasets, while discretizing reasoning decisions over the data.", "target": ["离散转化器，它使用硬注意力来确保每一步只取决于一个固定的环境。", "本文介绍了对标准转化器架构的修改，目的是提高可解释性，同时保留NLP任务的性能。", "本文提出了三个离散变换器：一个基于离散和随机的Gumbel-Softmax注意力模块，一个双流句法和语义变换器，以及稀疏性正则化。"]}
+{"source": "Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex.We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019).In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.", "target": ["我们展示了经验证据，预测性编码模型���生的表征比监督性图像识别模型与大脑数据更相关。"]}
+{"source": "The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples.Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest.Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion.We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task.Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations.The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources.Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.", "target": ["使用具有特定任务和共享权重的自动编码器对处理转移和多任务学习的通用框架。", "提出了一个用深度神经网络进行端到端转移学习/领域适应的通用框架。", "本文提出了一个允许深度神经网络架构在不同数据集上共享参数的模型，并将其应用于转移学习。", "本文着重于从多个领域的数据中学习共同特征，最后提出了一个用于多任务、半监督和转移学习的通用架构。"]}
+{"source": "Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers).ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects.We demonstrate this on classification and regression tasks, achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and outperforming standard neural networks, random forests and gradient boosted trees on the SARCOS dataset.Furthermore, ANT optimisation naturally adapts the architecture to the size and complexity of the training data.", "target": ["我们提出了一个结合决策树和神经网络的框架，并在图像分类任务中表明它享有两种方法的互补优势，同时解决了先前工作的局限性。", "作者通过将神经网络的表征学习和梯度优化与决策树的架构学习相结合，提出了一个新的模型--自适应神经树。", "本文提出了自适应神经树的方法，将深度神经网和决策树这两种学习规范结合起来。"]}
+{"source": "While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language.XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark.With improvements of up to 4.8, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu.XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.On the SQuAD question answering task, we see that XLDA provides a 1.0 performance increase on the English evaluation set.Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.", "target": ["在训练期间翻译部分输入内容可以提高跨语言的性能。", "本文提出了一种跨语言的数据增强方法，以改善语言推理和问题回答任务。", "本文提出用启发式交换来增强跨语言数据，使用联合的翻译，就像双语人类在代码转换中做的那样。"]}
+{"source": "Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse. We find, and empirically show, that one of the major reasons behind posterior collapse is rooted in the way that generative models are conditioned, i.e., through concatenation of the latent variable and the condition. To mitigate this problem, we propose to explicitly make the latent variables depend on the condition by unifying the conditioning and latent variable sampling, thus coupling them so as to prevent the model from discarding the root of variations. To achieve this, we develop a conditional Variational Autoencoder architecture that learns a distribution not only of the latent variables, but also of the condition, the latter acting as prior on the former. Our experiments on the challenging tasks of conditional human motion prediction and image captioning demonstrate the effectiveness of our approach at avoiding posterior collapse. Video results of our approach are anonymously provided in http://bit.ly/iclr2020", "target": ["我们提出了一个条件变异自动编码器框架，在条件信号强大到足以让表达式解码器从中产生可信的输出的情况下，可以缓解后验坍塌。", "本文考虑了强条件生成模型，并提出了一个目标函数和变异分布的参数化，从而使潜变量明确地取决于输入条件。", "本文认为，当解码器以潜在变量和辅助信息的串联为条件时，那么后验坍塌的可能性比普通的VAE更大。"]}
+{"source": "We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.  We propose a methodology for testing for statistical differences in model performances under several replications.To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.We find that the selected implementations exhibit stability across random seed, and repeats.", "target": ["我们提出了一项研究，即在控制随机种子的同时，受制于超参数和优化方案的变化，研究几种少样本学习算法的稳定性。", "本文研究的是少样本学习的可重复性。"]}
+{"source": "We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach.Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial.To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation.We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice.Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.", "target": ["我们在分层强化学习的背景下，将对表征的次优性的约束转化为实际的训练目标。", "作者提出了一种学习HRL表征的新方法，并指出了表征学习和约束次优性之间的有趣联系，从而形成了一种基于梯度的算法。", "本文提出了一种处理学习表征背景下的次优性的方法，它指的是分级政体相对于任务奖励的次优性。"]}
+{"source": "Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time.In online planning, algorithms for real-time search or deadline-aware search have been considered before.However, in this paper, we are interested in the problem of {\\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.  Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner.Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.", "target": ["坐标式时空规划器中的元推理", "本文讨论了位置时间规划的问题，��出了对Shperberg之前提出的贪婪策略的进一步简化。"]}
+{"source": "Neural networks are vulnerable to small adversarial perturbations.Existing literature largely focused on understanding and mitigating the vulnerability of learned models.In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier.Empirical investigations further confirm our finding.We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.", "target": ["PGD训练的模型的稳定性表现对图像数据集的语义保存转换很敏感，这意味着在实践中对稳定学习算法的评估很棘手。", "论文澄清了干净和稳定的准确性之间的区别，并表明在保留其语义P(y|x)的同时改变输入数据的边际分布会影响模型的稳定性。", "本文研究了在l-inf有界扰动下，分类器对对抗性输入的扰动缺乏稳定性的根源。"]}
+{"source": "Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity.Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence.When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors.We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures.Using a structured attention mechanism, our model matches possible spans in the first sentence to possible spans in the second sentence, simultaneously discovering the tree structure of each sentence and performing a comparison, in a model that is fully differentiable and is trained only on the comparison objective.We evaluate this model on two sentence comparison tasks: the Stanford natural language inference dataset and the TREC-QA dataset.We find that comparing spans results in superior performance to comparing words individually, and that the learned trees are consistent with actual linguistic structures.", "target": ["通过学习潜在的成分树结构，用嵌入为神经网络层的内-外算法的变体来匹配句子。", "本文介绍了一种结构化的关注机制，以计算两个给定句子中所有可能的跨度之间的对齐分数。", "本文提出了一个句子之间的结构化对齐模型，作为通过匹配句子的潜在结构进行比较的手段。"]}
+{"source": "Learning disentangled representation from any unlabelled data is a non-trivial problem.In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion.We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.", "target": ["以无监督的方式学习disentangle表征。", "作者提出了一个框架，在这个框架中，自动编码器（E，D）被正则化，使其潜在表示与生成的潜在空间表示共享相互信息。"]}
+{"source": "Effective training of neural networks requires much data.In the low-data regime,parameters are underdetermined, and learnt networks generalise poorly.DataAugmentation (Krizhevsky et al., 2012) alleviates this by using existing datamore effectively.However standard data augmentation produces only limitedplausible alternative data.Given there is potential to generate a much broader setof augmentations, we design and train a generative model to do data augmentation.The model, based on image conditional Generative Adversarial Networks, takesdata from a source domain and learns to take any data item and generalise itto generate other within-class data items.As this generative process does notdepend on the classes themselves, it can be applied to novel unseen classes of data.We show that a Data Augmentation Generative Adversarial Network (DAGAN)augments standard vanilla classifiers well.We also show a DAGAN can enhancefew-shot learning systems such as Matching Networks.We demonstrate theseapproaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, andVGG-Face data.In our experiments we can see over 13% increase in accuracy inthe low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot weobserve an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% inEMNIST (from 59.5% to 61.3%).", "target": ["有条件的GANs可以生成其条件输入的数据增强样本，用于增强普通分类和单样本学习系统，如匹配网络和像素距离。", "作者提出了一种进行数据增加的方法，即使用条件GAN将跨类转换映射到低维潜在空间中。"]}
+{"source": "Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model.For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables.However, randomization tests require users to specify test statistics.We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known.We show that f-divergences provide a broad class of proper test statistics.In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI.Questions of feature importance can be asked at the level of an individual sample.  We show that estimators from the same AMI test can also be used to find important features in a particular instance.We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection.We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet.Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.", "target": ["我们开发了一个简单的基于回归的模型诊断特征选择方法，以解释具有FDR控制的数据生成过程，并在几个模拟、医疗和图像数据集上的表现超过了几个流行的基线。", "本文提出了条件随机化检验的实际改进和新的检验统计量，证明了f-发散性是一种可能的选择，并表明KL-发散性取消了一些条件分布。", "本文讨论了在输入中寻找有用的特征的问题，这些特征即使在以所有其他输入变量为条件时也取决于反应变量。", "一种与模型无关的方法，提供关于输入特征对机器级模型反应的影响的解释，并为与模型无关的特征选择提供适当的测试统计数据。"]}
+{"source": "Supervised learning depends on annotated examples, which are taken to be the ground truth.But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk.Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem).Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples.This raises two fundamental questions: (1) How can we best learn from noisy workers?(2) How should we allocate our labeling budget to maximize the performance of a classifier?We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality.Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality.We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold.Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.", "target": ["一种从嘈杂的众包注释中学习模型的新方法。", "本文提出了一种从噪声标签中学习的方法，侧重于数据没有冗余标签的情况，并进行了理论和实验验证", "本文重点关注从众学习问题，联合更新分类器权重和工人的混淆矩阵可以帮助解决稀有众包标签的估计问题。", "提出了一种监督学习算法，用于对标签和工人的质量进行建模，并利用该算法研究在众包中需要多少多余��以及低多余度和大量的噪音实例是否会带来更好的标签。"]}
+{"source": "Neural networks make mistakes.The reason why a mistake is made often remains a mystery.As such neural networks often are considered a black box.It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.In this paper we explain our method and demonstrate it on MNIST and CelebA.This approach could aid in demystifying neural networks for a user.", "target": ["解释神经网络为何对图像分类有误的新方法", "本文提出了一种解释神经网络的分类错误的方法。", "旨在更好地理解神经网络的分类，探索变异自动编码器的潜空间，并考虑潜空间的扰动，以获得正确的分类。"]}
+{"source": "In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers.Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome.Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive.In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities.Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.", "target": ["一种自动构建分叉多任务网络的方法，在不同的多任务数据集上进行了强有力的实验评估。", "本文提出了一种基于树状结构的新型软参数共享多任务学习框架。", "本文提出了一种推断多任务网络结构的方法，以确定网络的哪一部分应该在不同任务之间共享。"]}
+{"source": "Typical recent neural network designs are primarily convolutional layers, but the tricks enabling structured efficient linear layers (SELLs) have not yet been adapted to the convolutional setting.We present a method to express the weight tensor in a convolutional layer using diagonal matrices, discrete cosine transforms (DCTs) and permutations that can be optimised using standard stochastic gradient methods.A network composed of such structured efficient convolutional layers (SECL) outperforms existing low-rank networks and demonstrates competitive computational efficiency.", "target": ["可以替换卷积层中的权重矩阵，当作结构化的有效层来训练；表现得和低秩分解一样好。", "这项工作将以前的结构化高效线性层应用于凸层，并提出结构化高效卷积层作为原始凸层的替代。"]}
+{"source": "Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc.Since this problem is highly ill-posed, supervised and unsupervised learning methods are well suited for this application.Using various techniques, extensive work has been done on natural-scene deblurring.However, these extracted features are not suitable for document images.We present SVDocNet, an end-to-end trainable U-Net based spatial recurrent neural network (RNN) for blind document deblurring where the weights of the RNNs are determined by different convolutional neural networks (CNNs).This network achieves state of the art performance in terms of both quantitative measures and qualitative results.", "target": ["我们提出了SVDocNet，一个基于空间循环神经网络（RNN）的端到端可训练的U-Net，用于盲文档去模糊。"]}
+{"source": "In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations.Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos.We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images.Our learning procedure leverages smooth motion in natural videos.Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner.The learned \"dynamic filters\" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames.Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary \"capsules\" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.", "target": ["我们扩展了双线性稀疏编码并利用视频序列来学习动态过滤器。"]}
+{"source": "Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) are known to assign lower uncertainty to the OOD data than the target distribution.In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images.Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training.Our detector is simple, efficient in test time, and outperforms baseline OOD detectors in various domains.Further results show that SVD-RND learns a better target distribution representation than the baselines.Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy in CelebA domain.", "target": ["我们提出了一种新型的OOD检测器，采用模糊的图像作为对抗性例子。我们的模型在各个领域都取得了显著的OOD检测性能。", "本文提出了使用模糊图像作为正则化实例的想法，以提高基于随机网络蒸馏法的分布外检测性能。", "本文通过利用应用于数据增量的RND来解决数据外分布问题，通过训练一个模型来匹配有增量输入的随机网络的输出。"]}
+{"source": "Training large deep neural networks on massive datasets is  computationally very challenging.There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue.The most prominent algorithm in this line of research is LARS, which by  employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes.However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks.In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning.In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance.  By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).", "target": ["一个用于一般应用和大批量训练的快速优化器。", "在本文中，作者对BERT的大批量训练进行了研究，并在76分钟内成功训练了一个BERT模型。", "本文开发了一种分层适应策略，允许用大的32k小批与基线512训练BERT模型。"]}
+{"source": "Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.However, MAML is notorious for being hard to train because of the existence of two learning rates.Therefore, in this paper, we derive the conditions that inner learning rate $\\alpha$ and meta-learning rate $\\beta$ must satisfy for MAML to converge to minima with some simplifications.We find that the upper bound of $\\beta$ depends on $ \\alpha$, in contrast to the case of using the normal gradient descent method.Moreover, we show that the threshold of $\\beta$ increases as $\\alpha$ approaches its own upper bound.This result is verified by experiments on various few-shot tasks and architectures; specifically, we perform sinusoid regression and classification of Omniglot and MiniImagenet datasets with a multilayer perceptron and a convolutional neural network.Based on this outcome, we present a guideline for determining the learning rates: first, search for the largest possible $\\alpha$; next, tune $\\beta$ based on the chosen value of $\\alpha$.", "target": ["我们分析了两种学习率在模型诊断���元学习中的收敛作用。", "作者通过研究两种学习率，解决了MAML中的优化不稳定问题。", "本文研究了一种帮助调整MAML训练算法中使用的两种学习率的方法。"]}
+{"source": "We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity.The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.", "target": ["学习相互关联的词组之间联系的任务无关的神经模型。", "该论文提出了一种训练特定功能词向量的方法，其中每个词用三个向量表示，每个向量都是不同的类别（主语-动词-宾语）。", "本文提出了一个神经网络来学习特函数能的工作表征，并证明了与其他方法相比的优势。"]}
+{"source": "The fabrication of semiconductor involves etching process to remove selected areas from wafers.However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines.Traditional image processing usually demands on large number of annotated data and the performance is still poor.We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer.Then, we use simple image processing to carry out automatic measurement on the objects.We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset.We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments.Our deep learning based method demonstrates superiority over image processing approach with  mean accuracy reaching over 96% for the measurements, compared with the ground truth.To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.", "target": ["利用深度学习方法对半导体行业的SEM图像进行自动测量"]}
+{"source": "Generating and scheduling activities is particularly challengingwhen considering both consumptive resources andcomplex resource interactions such as time-dependent resourceusage.We present three methods of determining validtemporal placement intervals for an activity in a temporallygrounded plan in the presence of such constraints.We introducethe Max Duration and Probe algorithms which aresound, but incomplete, and the Linear algorithm which issound and complete for linear rate resource consumption.We apply these techniques to the problem of schedulingawakes for a planetary rover where the awake durationsare affected by existing activities.We demonstrate how theProbe algorithm performs competitively with the Linear algorithmgiven an advantageous problem space and well-definedheuristics.We show that the Probe and Linear algorithmsoutperform the Max Duration algorithm empirically.We then empirically present the runtime differences betweenthe three algorithms.The Probe algorithm is currently base-linedfor use in the onboard scheduler for NASA’s next planetaryrover, the Mars 2020 rover.", "target": ["本文描述并分析了在存在消耗性资源的情况下安排非固定期限活动的三种方法。", "本文介绍了在水库资源约束下，行星探测器的车载活动调度的三种方法。"]}
+{"source": "A disentangled representation of a data set should be capable of recovering the underlying factors that generated it.One question that arises is whether using Euclidean space for latent variable models can produce a disentangled representation when the underlying generating factors have a certain geometrical structure.Take for example the images of a car seen from different angles.The angle has a periodic structure but a 1-dimensional representation would fail to capture this topology.How can we address this problem?The submissions presented for the first stage of the  NeurIPS2019 Disentanglement Challenge consist of a Diffusion Variational Autoencoder ($\\Delta$VAE) with a hyperspherical latent space which can for example recover periodic true factors.The training of the $\\Delta$VAE is enhanced by incorporating a modified version of the Evidence Lower Bound (ELBO) for tailoring the encoding capacity of the posterior approximate.", "target": ["向NeurIPS2019年基于超球面变异自动编码器的解缠挑战提交的说明"]}
+{"source": "Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.Recently, classification-based methods were shown to achieve superior results on this task.In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions.Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations.Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types.The strong performance of our method is extensively validated on multiple datasets from different domains.", "target": ["一种异常检测：使用随机变换分类来泛化到非图像数据。", "本文提出了一种用于异常检测的深度方法，该方法统一了最近的深度单类分类和基于转化的分类方法。", "本文提出了一种通过使用仿射变换y=Wx+b对一般数据进行基于分类的异常检测的方法。"]}
+{"source": "Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications.Many of these advances leverage the availability of large corpora.While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora.This paper aims to quantify and reduce biases exhibited by language models.Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation.We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature.Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data.We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.", "target": ["我们在使用语言模型对文本生成进行反事实评估的基础上，减少情感偏差。", "本文测量了由模型生成的文本所反映的语言模型中的情感偏差，并在通常的语言模型目标中加入了其他的目标条款以减少偏差。", "本文提出通过使用固定的情感系统来评估预训练的语言模型的偏差，并测试了几种不同的前缀模板。", "一种基于语义相似性的方法和一种基于情感相似性的方法，对从大型数据集训练出来的神经语言模型进行去偏差。"]}
+{"source": "Topic modeling of text documents is one of the most important tasks in representation learning.In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders.On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically.On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically.Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks.Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.", "target": ["一种带有变分自动编码器的贝叶斯非参数主题模型，在困惑度、主题一致性和检索任务方面达到了公共基准的先进水平。", "本文通过将Nalisnick & Smith的破棍变分自动编码器与潜在的Dirichlet分配和Miao中使用的几种推理技术相结合，构建了一个具有变分自动编码器的无限主题模型。"]}
+{"source": "Knowledge Distillation (KD) is a widely used technique in recent deep learning research to obtain small and simple models whose performance is on a par with their large and complex counterparts.Standard Knowledge Distillation tends to be time-consuming because of the training time spent to obtain a teacher model that would then provide guidance for the student model.It might be possible to cut short the time by training a teacher model on the fly, but it is not trivial to have such a high-capacity teacher that gives quality guidance to student models this way.To improve this, we present a novel framework of Knowledge Distillation exploiting dark knowledge from the whole training set.In this framework, we propose a simple and effective implementation named Distillation by Utilizing Peer Samples (DUPS) in one generation.We verify our algorithm on numerous experiments.Compared with standard training on modern architectures, DUPS achieves an average improvement of 1%-2% on various tasks with nearly zero extra cost.Considering some typical Knowledge Distillation methods which are much more time-consuming, we also get comparable or even better performance using DUPS.", "target": ["我们提出了一个新的知识提炼框架，利用同行的样本作为教师的知识蒸馏。", "提出了一种通过软化所使用的标签和采用数据集而不是单一样本来提高知识蒸馏的有效性的方法。", "本文在最近提出的快照蒸馏技术的基础上，提出用知识蒸馏来解决训练的额外计算成本。"]}
+{"source": "We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps.Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.", "target": ["通过对任务分布的端到端训练来学习分层的子策略", "作者考虑了学习一套有用的 \"子策略 \"的问题，这套策略可以在任务之间共享，以便快速开始学习从任务分布中抽取的新任务。 ", "本文提出了一种在专门的多任务设置中诱导时间层次结构的新方法。"]}
+{"source": "This paper proposes a new model for document embedding.Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize.We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model.This allows us to train deeper architectures that are fully parallelizable.Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document.Empirically we demonstrate superior results on two publicly available benchmarks.Full code will be released with the final version of this paper.", "target": ["用于无监督文档嵌入的卷积神经网络模型。", "介绍了一种用于诱导文档表征（嵌入）的一般任务的新模型，该模型使用CNN架构来提高计算效率。", "本文提出使用具有类似skip-gram目标的CNN作为输出文档嵌入的快速方法。"]}
+{"source": "We prove bounds on the generalization error of convolutional networks.The bounds are in terms of the training loss, the number ofparameters, the Lipschitz constant of the loss and the distance fromthe weights to the initial weights. They are independent of thenumber of pixels in the input, and the height and width of hiddenfeature maps. We present experiments with CIFAR-10, along with varyinghyperparameters of a deep convolutional network, comparing our boundswith practical generalization gaps.", "target": ["我们证明了考虑到权重配对的卷积神经网络的泛化边界", "研究了CNN的泛化能力，并改进了泛化误差的上限，显示了学习型CNN的泛化误差与上限的主导项之间的相关性。", "本文基于参数数量、Lipschitz常数和最终权重与初始化的距离，提出了卷积神经网络的泛化边界。"]}
+{"source": "MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years.New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks.Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 − 6 bits albeit with a modest to significant drop in accuracy.While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs.Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets.The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets.Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.", "target": ["在ImageNet上使用由传统全精度过滤器和三元过滤器组成的混合层，在不损失精度的情况下，模型大小节省了2倍，MobileNets的能量减少了28%。", "专注于将MobileNets架构量化为三元值，降低所需空间和计算量，以使神经网络更加节能。", "本文提出了层级混合滤波器库，它只将卷积滤波器的一部分量化为三元值，以适应MobileNets架构。"]}
+{"source": "Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting.To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels.As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings.Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise.(2) DNNs may not learn patterns first on real-world noisy data.(3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data.(4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.(5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa.We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.", "target": ["我们建立了一个受控的真实噪声的基准，并揭示了关于真实世界噪声数据的几个有趣的发现。", "本文在两种训练环境下比较了6种现有的噪声标签学习方法：从零开始和微调。", "作者建立了一个大型的数据集和受控的真实世界噪声的基准，用于对深度学习中的噪声数据进行受控实验。"]}
+{"source": "Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation.Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints.Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA.LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure.By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks.Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process.Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance.In an ablation study, we analyze the importance of our method's different components.", "target": ["我们学习使用元学习和autoML方法，用强化学习解决RNA设计问题。", "使用策略梯度优化来生成折叠成靶二级结构的RNA序列，从而明显提高了准确性和运行时间。"]}
+{"source": "Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed.However, the value of pruning has largely evaded scrutiny.In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations.First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network.Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable.Such architectures are powerful when trained from scratch.Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.", "target": ["训练小网络胜过修剪，但修剪能找到好的小网络来训练，而且容易复制。"]}
+{"source": "Supervised learning problems---particularly those involving social data---are often subjective.That is, human readers, looking at the same data, might come to legitimate but completely different conclusions based on their personal experiences.Yet in machine learning settings feedback from multiple human annotators is often reduced to a single ``ground truth'' label, thus hiding the true, potentially rich and diverse interpretations of the data found across the social spectrum.We explore the rewards and challenges of discovering and learning representative distributions of the labeling opinions of a large human population.A major, critical cost to this approach is the number of humans needed to provide enough labels not only to obtain representative samples but also to train a machine to predict representative distributions on unlabeled data.We propose aggregating label distributions over, not just individuals, but also data items, in order to maximize the costs of humans in the loop.We test different aggregation approaches on state-of-the-art deep learning models.Our results suggest that careful label aggregation methods can greatly reduce the number of samples needed to obtain representative distributions.", "target": ["我们研究的问题是学习预测监督学习领域中存在的潜在的信仰多样性。"]}
+{"source": "Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images.While much more effective than conventional approaches, deep learning models require large datasets and great computational resources for training, and inpainting quality varies considerably when training data vary in size and diversity.To address these problems, we present in this paper a inpainting strategy of \\textit{Comparative Sample Augmentation}, which enhances the quality of training set by filtering out irrelevant images and constructing additional images using information about the surrounding regions of the images to be inpainted.Experiments on multiple datasets demonstrate that our method extends the applicability of deep inpainting models to training sets with varying sizes, while maintaining inpainting quality as measured by qualitative and quantitative metrics for a large class of deep models, with little need for model-specific consideration.", "target": ["我们引入了一种战略，可以在不同大小的数据集上对模型进行绘画。", "通过使用比较增强型过滤器并在每个像素上添加随机噪声，帮助使用GAN进行图像inpainting。"]}
+{"source": "Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion.Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost.GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players’ parameters.One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium.Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence.We show that this view is overly restrictive.During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful.We provide empirical counterexamples to the view of GAN training as divergence minimization.Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail.We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful.This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.", "target": ["我们发���有证据表明发散最小化可能不是对GAN训练的准确描述。", "本报告旨在提出实证性证据，证明发散最小化理论更多的是理解训练GAN的结果的工具，而不是在训练期间必须执行的必要条件。", "本文研究了非饱和GAN和两种惩罚性梯度方法的效果，考虑了几个思想实验来证明观察结果，并在真实数据实验上进行了验证。"]}
+{"source": "Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.Recent works have developed accurate MI estimators through provably low-bias approximations and tight variational lower bounds assuming abundant supply of samples, but require an unrealistic number of samples to guarantee statistical significance of the estimation.In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE) that can provide a tight lower confident interval of MI under limited data, through adding cross-validation to the MINE lower bound (Belghazi et al., 2018).Hyperparameter search is employed and a novel meta-learning approach with task augmentation is developed to increase robustness to hyperparamters, reduce overfitting and improve accuracy.With improved data-efficiency, our DEMINE estimator enables statistical testing of dependency at practical dataset sizes.We demonstrate the effectiveness of DEMINE on synthetic benchmarks and a real world fMRI dataset, with application of inter-subject correlation analysis.", "target": ["使用神经网络对依赖性进行新的和实用的统计测试，以合成和真实的fMRI数据集为基准。", "提出了一种基于神经网络的相互信息估计方法，该方法可以可靠地处理小数据集，通过解耦网络学习问题和估计问题来降低样本复杂性。"]}
+{"source": "Language and vision are processed as two different modal in current work for image captioning.However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem.In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.The experimental results on Flickr30k data shows the proposed method gives high quality image captions.An interactive demo is ready to show at the workshop.", "target": ["使用二维词汇嵌入的图像字幕。"]}
+{"source": "Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.However, choosing a non-trivial scheduling method may drastically improve convergence.In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).Our method parameterizes mini-batches dynamically based on the \\textit{easiness} and \\textit{true diverseness} of the sample within a salient feature representation space.In LEAP, we train an \\textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.The \\textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \\textit{easiness} from cross-entropy losses and \\textit{true diverseness} of examples from the representation space sculpted by the \\textit{embedding} CNN.We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.", "target": ["LEAP将自适应采样的优势与小批量在线学习和自适应表征学习的优势相结合，在端到端DNN训练协议中制定了一个有代表性的自适应策略。", "介绍了一种为学生网络创建小型批次的方法，通过使用第二种学习的表征空间，以其 \"易用性和真正的多样性 \"动态地选择例子。", "实验了MNIST、FashionMNIST和CIFAR-10数据集的分类准确率，从而在一个端到端的框架中，用课程学习方式的小型批次选择来学习一个表征。"]}
+{"source": "Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments.To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space.The problem lies in how to find an appropriate macro action to augment the primitive action space.  The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure.In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions.However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy.On the other hand, repeating actions may limit the diversity of behaviors of the agent.Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.  Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not.   We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods.Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments.We further provide a comprehensive set of ablation analysis to validate our methodology.", "target": ["我们建议通过遗传算法来构建宏观行动，这就消除了宏观行动推导过程对代理过去策略的依赖性。", "本文提出了一种通用算法，通过在原始动作空间中附加一个宏观动作来构建深度强化学习的宏观动作。"]}
+{"source": "A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients.A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process.Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system.We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.", "target": ["我们提出了一个对LFADS的扩展，能够推断出尖峰序列，利用层次化的VAE重建钙荧光痕迹。"]}
+{"source": "In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal.In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora.Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation.Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation.The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively.Our implementation is released as an open source project.", "target": ["我们介绍了第一个成功的方法，以无监督的方式训练神经机器翻译，只使用单语语料。", "作者提出了一个无监督的NMT模型，该模型不需要两种相关语言的平行语料。", "这是一篇关于无监督MT的论文，它只用双语论文和使用单语数据训练的编码器-解码器，在共享嵌入空间中使用词嵌入来训练一个标准架构。"]}
+{"source": "We describe a new training methodology for generative adversarial networks.The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2.We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10.Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator.Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation.As an additional contribution, we construct a higher-quality version of the CelebA dataset.", "target": ["我们以渐进的方式训练生成式对抗网络，���我们能够生成高质量的高分辨率图像。", "介绍了渐进式增长和一个简单的无参数小型批次总结统计特征，用于GAN训练，以实现高分辨率图像的合成。"]}
+{"source": "Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance.DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata.This contribution is twofold.First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors.Second, we evaluate DeepSphere on relevant problems.Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation.Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.", "target": ["一个基于图形的球形CNN，在各种应用的权衡中取得了有趣的平衡。", "结合现有的基于球体离散化为图形的CNN框架，展示了一个与球体上的旋转等价性有关的收敛结果。", "作者使用了现有的图CNN表述和一种池化战略，利用球体的分层像素化，从离散的球体中学习。"]}
+{"source": "The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics.In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity.Here, we derive stationary fluctuation-dissipation relations that link measurable quantities and hyperparameters in the stochastic gradient descent algorithm.These relations hold exactly for any stationary state and can in particular be used to adaptively set training schedule.We can further use the relations to efficiently extract information pertaining to a loss-function landscape such as the magnitudes of its Hessian and anharmonicity.Our claims are empirically verified.", "target": ["我们证明了SGD的波动-消散关系，这可以用来(i)自适应地设置学习率和(ii)探测损失面。", "论文的概念在离散时间形式主义中工作，使用主方程，并消除了对损失函数的局部二次近似或对SGD噪声的任何高斯假设的依赖。", "作者推导出静止的波动-消散关系，该关系将SGD中的可测量量和超参数联系起来，并利用该关系自适应地设置训练计划和分析损失函数景观。"]}
+{"source": "Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise.We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance.Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence.The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states.This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step.On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss.We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.", "target": ["我们提出了一种对RNN的内部状态进行去噪的机制，以提高泛化性能。"]}
+{"source": "We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking.Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns.Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training.We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines.We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs.Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.", "target": ["对于部分由外部输入过程决定的环境，我们推导出一个输入依赖的基线，该基线可证明降低了策略梯度方法的方��，并提高了广泛的RL任务中的策略性能。", "作者考虑了在输入驱动环境下的学习问题，显示了PG定理如何仍然适用于输入感知的批评家，并表明输入依赖的基线是在与该批评家的猜想中使用的最佳选择。", "本文介绍了RL中策略梯度方法中输入依赖基线的概念，并提出了不同的方法来训练输入依赖基线函数，以帮助清除外部因素扰动的方差。"]}
+{"source": "Deep networks have shown great performance in classification tasks.However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification.We introduce a network that has the capacity to do both classification and reconstruction by adding a \"style memory\" to the output layer of the network.We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses.The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct.We further investigate the nature of the style memory, and how it relates to composing digits and letters.", "target": ["用样式记忆增强分类器网络的顶层，可以使其具有生成性。", "本文提出，训练一个分类器神经网络不仅要进行分类，还要重建其输入的表征，以便从外观上对分类信息进行因子化。", "本文提出训练一个自动编码器，使中间层的表示包括输入的类别标签和一个隐藏的向量表示。"]}
+{"source": "Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works.Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions.Both architectural diversity and routing depth can increase the representational power of a routing network.In this work, we address both of these deficiencies.We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth.In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup.However, when scaling up routing depth, we find that modern routing techniques struggle with optimization.We conclude by discussing both the positive and negative results, and suggest directions for future research.", "target": ["每个例子的路由模型得益于架构的多样性，但仍然难以扩展到大量的路由决策。", "为路由器在每次决策时可用的架构单元类型增加了多样性，并可扩展到更深的网络，在Omniglot上实现最先进的性能。", "这项工作扩展了路由网络，使其在整个路由模块中使用多样化的架构"]}
+{"source": "Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs).Although the use of deep-learning techniques has been proposed, the uses have been restricted by the fact the training data are obtained using PDE solvers.Thereby, the uses were limited to domains, where the PDE solver was applicable, but no further. We present methods for training on small domains, while applying the trained models on larger domains, with consistency constraints ensuring the solutions are physically meaningful even at the boundary of the small domains.We demonstrate the results on an air-pollution forecasting model for Dublin, Ireland.", "target": ["我们提出了用于训练PDE代用模型的RNN，其中一致性约束确保了解决方案在物理上是有意义的，即使训练使用的域比训练的模型小得多。"]}
+{"source": "We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs.Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games.WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics.  Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs.We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle.We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum.We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences.We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants.Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam.We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.", "target": ["我们提出使用乐观的镜像体面来解决GAN训练中的循环问题。我们还介绍了乐观的亚当算法", "本文提出使用乐观的镜像下降法来训练WGAN", "本文提出使用乐观的梯度下降法进行GAN训练，避免了SGD及其变体所观察到的循环行为，并在GAN训练中提供了有希望的结果。", "本文提出了一个对标准梯度下降的简单修改，声称可以改善GAN和其他最小优化问题的收敛性。"]}
+{"source": "Learning good representations of users and items is crucially important to recommendation with implicit feedback.Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix.However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information.In this paper, we propose a novel Augmented Generalized Matrix Factorization (AGMF) approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items.Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts.Furthermore, the ablation study demonstrates that by using multi-hot encoding to enrich user embedding and item embedding for Generalized Matrix Factorization, better performance, faster convergence, and lower training loss can be achieved.", "target": ["广义矩阵分解的一个简单扩展可以超越最先进的推荐方法。", "这项工作提出了一个矩阵分解框架，用于在协作过滤设置中学习用户偏好时强制执行历史数据的效果。"]}
+{"source": "We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions.The method simultaneously acquires representations of input data and its dynamics.It is based on a hierarchical generative model composed of two levels.In the first level, a model learns representations to generate observed data.In the second level, representational states encode the dynamics of the lower one.The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models.The method actively explores the latent space guided by its knowledge and the uncertainty about it.That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space.So, no encoder or inference models are used since the generators also serve as their inverse transformations.The method is evaluated in two scenarios, with static images and with videos.The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders.With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.", "target": ["一种通过生成模型建立顺序数据及其动态的表征的方法，具有主动的过程", "结合神经网络和高斯分布，为图像和视频创建一个架构和生成模型，使生成的图像和提供的图像之间的误差最小。", "本文提出一个贝叶斯网络模型，以神经网络的形式实现，以线性动态系统的形式学习不同的数据"]}
+{"source": "Activation is a nonlinearity function that plays a predominant role in the convergence and performance of deep neural networks.While Rectified Linear Unit (ReLU) is the most successful activation function, its derivatives have shown superior performance on benchmark datasets.In this work, we explore the polynomials as activation functions (order ≥ 2) that can approximate continuous real valued function within a given interval.Leveraging this property, the main idea is to learn the nonlinearity, accepting that the ensuing function may not be monotonic.While having the ability to learn more suitable nonlinearity, we cannot ignore the fact that it is a challenge to achieve stable performance due to exploding gradients - which is prominent with the increase in order.To handle this issue, we introduce dynamic input scaling, output scaling, and lower learning rate for the polynomial weights.Moreover, lower learning rate will control the abrupt fluctuations of the polynomials between weight updates.In experiments on three public datasets, our proposed method matches the performance of prior activation functions, thus providing insight into a network’s nonlinearity preference.", "target": ["我��提出多项式作为激活函数。", "作者介绍了以多项式函数为参数的可学习激活函数，并显示出比ReLU略好的结果。"]}
+{"source": "We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal.CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space.It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders.We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline.", "target": ["一种简单的内在激励方法，使用策略的特征空间中的前向动力学模型误差。"]}
+{"source": "This paper is concerned with the robustness of VAEs to adversarial attacks.We highlight that conventional VAEs are brittle under attack but that methods recently introduced for disentanglement such as β-TCVAE (Chen et al., 2018) improve robustness, as demonstrated through a variety of previously proposed adversarial attacks (Tabacof et al. (2016); Gondim-Ribeiro et al. (2018); Kos et al.(2018)).This motivated us to develop Seatbelt-VAE, a new hierarchical disentangled VAE that is designed to be significantly more robust to adversarial attacks than existing approaches, while retaining high quality reconstructions.", "target": ["我们表明，对于旨在欺骗它们将对抗性输入解码为选定目标的对抗性攻击，分解的VAE比虚构的VAE更加稳健。然后，我们开发了一个更加稳健的分层分解VAE，即Seatbelt-VAE。", "作者提出了一个新的VAE模型，称为 \"Seatbelt-VAE\"，显示出对潜伏攻击比基准更加稳健。"]}
+{"source": "The backpropagation algorithm is the de-facto standard for credit assignment in artificial neural networks due to its empirical results.Since its conception, variants of the backpropagation algorithm have emerged.More specifically, variants that leverage function changes in the backpropagation equations to satisfy their specific requirements.Feedback Alignment is one such example, which replaces the weight transpose matrix in the backpropagation equations with a random matrix in search of a more biologically plausible credit assignment algorithm.In this work, we show that function changes in the  backpropagation procedure is equivalent to adding an implicit learning rate to an artificial neural network.Furthermore, we learn activation function derivatives in the backpropagation equations to demonstrate early convergence in these artificial neural networks.Our work reports competitive performances with early convergence on MNIST and CIFAR10 on sufficiently large deep neural network architectures.", "target": ["我们证明，反向传播中的函数变化相当于一个隐性学习率"]}
+{"source": "Unsupervised text style transfer is the task of re-writing text of a given style into a target style without using a parallel corpus of source style and target style sentences for training.Style transfer systems are evaluated on their ability to generate sentences that1) possess the target style,2) are fluent and natural sounding, and3) preserve the non-stylistic parts (content) of the source sentence.We train a reinforcement learning (RL) based unsupervised style transfer system that incorporates rewards for the above measures, and describe novel rewards shaping methods for the same.Our approach does not attempt to disentangle style and content, and leverages the power of massively pre-trained language models as well as the Transformer.Our system significantly outperforms existing state-of-art systems based on human as well as automatic evaluations on target style, fluency and content preservation as well as on overall success of style transfer, on a variety of datasets.", "target": ["文本风格转移的强化学习方法", "介绍了一种基于RL的方法，该方法利用预先训练好的语言模型来转移文本风格，没有脱离目标，同时使用来自另一个模型的风格转移代系。", "作者提出了一个由流畅性、内容和风格组成的组合奖励，用于文本风格的转移。"]}
+{"source": "Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes.By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image.Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes.The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation.", "target": ["我们表明，高度结构化的语义层次出现在深度生成表征中，作为合成场景的结果。", "论文研究了输入到StyleGAN不同层的潜变量所编码的方面。", "本文对StyleGAN的生成器中的卷积层在布局、场景类别、场景属性和颜色上的激活进行了视觉引导的解释。"]}
+{"source": "Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions.To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, harmonizing hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation.By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior.Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.", "target": ["我们在同一分子的多个SMILES字符串中汇集信息，沿分子图的所有路径传递信息，产生的潜在表征在各种任务中大大超过了最先进的水平。", "该方法使用SMILES字符串的多个输入，在这些字符串中进行特征融合，并通过SMILES字符串的多个输出目标进行网络训练，创建一个独立于SMILES变化的稳健的固定长度的潜在表示。", "作者描述了一种类似于分子变分自动编码器的新方法，该方法将分子编码为字符串，以减少分子中各原子间共享信息所需的操作。"]}
+{"source": "We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative  Adversarial  Network (cGAN).Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code.To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes.The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives.Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity.We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction.We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.", "target": ["我们提出了一个简单而通用的方法，避免了各种条件GAN中的模式崩溃问题。", "本文为条件GAN目标提出了一个正则化项，以促进多样化的多模态生成并防止模式崩溃。", "本文提出了一种为各种条件性GAN框架生成多样化输出的方法，包括图像到图像的转换、图像绘画和视频预测，可以应用于各种条件性合成框架，用于各种任务。"]}
+{"source": "The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context.Lexical features are fed into the first layer and propagated through a deep network of hidden layers.We argue that the need to represent and propagate lexical features in each layer limits the model’s capacity for learning and representing other information relevant to the task.To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder.This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states.We show that the proposed modification yields consistent improvements on standard WMT translation tasks and reduces the amount of lexical information passed along the hidden layers.We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.", "target": ["为转化器模型配备通往嵌入层的捷径，释放了模型的能力以学习新信息。"]}
+{"source": "Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.  More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images.  We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions.  We perform sanity check experiments to provide evidence that these probabilities are reasonable.  However, we also show that density functions of natural images are difficult to interpret and thus limited in use.  We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images.", "target": ["我们研究了不可逆转的GAN中概率密度值和图像内容之间的关系。", "作者试图在GAN的帮助下估计图像的概率分布，并在潜伏空间中开发一个适当的PDF近似。"]}
+{"source": "Convolutional Neural Networks (CNNs) are composed of multiple convolution layers and show elegant performance in vision tasks.The design of the regular convolution is based on the Receptive Field (RF) where the information within a specific region is processed.In the view of the regular convolution's RF, the outputs of neurons in lower layers with smaller RF are bundled to create neurons in higher layers with larger RF. As a result, the neurons in high layers are able to capture the global context even though the neurons in low layers only see the local information.However, in lower layers of the biological brain, the information outside of the RF changes the properties of neurons.In this work, we extend the regular convolution and propose spatially shuffled convolution (ss convolution).In ss convolution, the regular convolution is able to use the information outside of its RF by spatial shuffling which is a simple and lightweight operation.We perform experiments on CIFAR-10 and ImageNet-1k dataset, and show that ss convolution improves the classification performance across various CNNs.", "target": ["我们提出了空间洗牌卷积，即常规卷积包含了来自其接受领域之外的信息。", "提出了使用其RF以外的信息的SS convulation，在对多个CNN模型进行测试时显示了更好的结果。", "作者提出了一种卷积神经网络中卷积层的洗牌战略。"]}
+{"source": "We propose a framework to model the distribution of sequential data coming froma set of entities connected in a graph with a known topology.The method isbased on a mixture of shared hidden Markov models (HMMs), which are trainedin order to exploit the knowledge of the graph structure and in such a way that theobtained mixtures tend to be sparse.Experiments in different application domainsdemonstrate the effectiveness and versatility of the method.", "target": ["一种对来自图连接实体的序列的生成分布进行建模的方法。", "作者提出了一种方法，使用HMM的共同池的混合物对来自多个相互联系的源的顺序数据进行建模。"]}
+{"source": "To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions.We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.To get an accurate model, many observations are needed and this can be sample-inefficient.What's more, the learned model and policy can overfit to current agents and cannot generalize if the other agents are replaced by new agents.In many practical situations, each agent we face can be considered as a sample from a population with a fixed but unknown distribution.Thus we can treat the task against some specific agents as a task sampled from a task distribution.We apply meta-learning method to build models and learn policies.Therefore when new agents come, we can adapt to them efficiently.Experiments on grid games show that our method can quickly get high rewards.", "target": ["我们的工作将元学习应用于多代理强化学习，以帮助我们的代理有效地适应新到来的对手。", "本文的重点是使用基于MAML的方法快速适应环境中其他代理的新行为。", "本文提出了一种基于模型无关的元学习框架的多代理学习方法，用于多代理RL的对手建模任务。"]}
+{"source": "We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.  This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball.We show that this is an effective regularizer;  for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%.", "target": ["我们描述了与标准二维多通道卷积层相关的线性变换的奇异值，使其能够得到有效计算。", "本文致力于计算卷积层的奇异值", "推导出计算深度神经网络卷积层奇异值的精确公式，并表明通过诉诸快速FFT变换，计算奇异值可以比计算卷积矩阵的全部SVD快得多。"]}
+{"source": "Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment.Computing a Bayes-optimal policy is however intractable for all but the smallest tasks.In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection.In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty.We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.", "target": ["VariBAD利用元学习、贝叶斯RL和近似变分推理的思想，为深度RL开辟了一条可操作的近似贝叶斯最优探索之路。", "本文提出了一种新的深度强化学习方法，可以有效地权衡探索和利用，它结合了元学习、变分推理和贝叶斯RL。"]}
+{"source": "In a continual learning setting, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories.While deep neural nets have achieved resounding success in the classical setting, they are known to forget about knowledge acquired in prior episodes of learning if the examples encountered in the current episode of learning are drastically different from those encountered in prior episodes.This makes deep neural nets ill-suited to continual learning.In this paper, we propose a new model that can both leverage the expressive power of deep neural nets and is resilient to forgetting when new categories are introduced.We demonstrate an improvement in terms of accuracy on original classes compared to a vanilla deep neural net.", "target": ["我们表明，指标学习可以帮助减少灾难性遗忘的发生", "本文通过提高最后一层的表现力，应用度量学习来减少神经网络上的灾难性遗忘，从而在持续学习中获得更好的结果。"]}
+{"source": "Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base.  We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document.NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference.  To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset.  Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.", "target": ["我们提出了NormCo，一个深度一致性模型，它考虑了实体提及的语义，以及单个文档中提及的主题一致性，以进行疾病实体的规范化。", "使用GRU自动编码器来表示 \"上下文\"（在一个句子的范围内与某一疾病相关的内容），解决了BioNLP的任务，比最知名的方法有明显的改进。"]}
+{"source": "We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated.We begin by showing that such layers strictly enrich the representable function classes of neural networks.We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required.We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation.Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.", "target": ["我们探索乘法互动的作用，将其作为一个统一的框架来描述一系列经典和现代的神经网络架构模式，如门控、注意力层、超网络和动态卷积等。", "提出了乘法交互作为代表常用模型架构设计组件的统一表征，显示了在RL和序列建模等任务上的卓越性能的实证证明。", "本文探讨了不同类型的乘法互动，发现MI模型能够在语言建模和强化学习问题上取得最先进的性能。"]}
+{"source": "Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning.In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a GAN model with novel conditioning scheme that aids improving the text-video associations.With a combination of this conditioning scheme and a deep GAN architecture, TFGAN generates photo-realistic videos from text on very challenging real-world video datasets.In addition, we construct a benchmark synthetic dataset of moving shapes to systematically evaluate our conditioning scheme.Extensive experiments demonstrate that TFGAN significantly outperforms the existing approaches, and can also generate videos of novel categories not seen during training.", "target": ["用于从文本中生成视频的有效文本调节GAN框架", "本文提出了一种基于GAN的视频生成方法，以文本描述为条件，采用一种新的调节方法，从编码的文本中生成卷积滤波器，并将其用于判别器的卷积。", "本文提出了用于文本到视频合成的条件性GAN模型：开发文本特征条件的CNN过滤器，并构建了在视频/图像生成方面具有改进性能的移动形状数据集。"]}
+{"source": "Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima.In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on \\emph{differential inclusions of inverse scale spaces}, that generates a family of models from simple to complex ones by coupling gradient descent and mirror descent to explore model structural sparsity.It has a simple discretization, called the Split Linearized Bregman Iteration (SplitLBI), whose global convergence analysis in deep learning is established that from any initializations, algorithmic iterations converge to a critical point of empirical risks.Experimental evidence shows that\\ SplitLBI may achieve state-of-the-art performance in large scale training on ImageNet-2012 dataset etc., while with \\emph{early stopping} it unveils effective subnet architecture with comparable test accuracies to dense models after retraining instead of pruning well-trained ones.", "target": ["SplitLBI被应用于深度学习，探索模型结构的稀疏性，在ImageNet-2012中取得了最先进的性能，并揭示了有效的子网结构。", "提出了一种基于优化的算法，通过耦合权重矩阵的学习和稀疏性约束来寻找大规模神经网络的重要稀疏结构，在非凸优化问题上提供保证收敛性。"]}
+{"source": "In this paper, we study the learned iterative shrinkage thresholding algorithm (LISTA) for solving sparse coding problems.  Following assumptions made by prior works, we first discover that the code components in its estimations may be lower than expected, i.e., require gains, and to address this problem, a gated mechanism amenable to theoretical analysis is then introduced.Specific design of the gates is inspired by convergence analyses of the mechanism and hence its effectiveness can be formally guaranteed.In addition to the gain gates, we further introduce overshoot gates for compensating insufficient step size in LISTA.Extensive empirical results confirm our theoretical findings and verify the effectiveness of our method.", "target": ["我们提出了门控机制，以增强稀疏编码的学习型ISTA，并在理论上保证该方法的优越性。", "提出了LISTA的扩展，通过引入 \"增益门 \"和包括 \"过冲门 \"的动量来解决低估问题，显示了改进的收敛率。", "本文主要是通过提出一个 \"增益门控函数 \"来解决使用LISTA型网络的稀疏编码问题，以缓解 \"无误报 \"假设的弱��。"]}
+{"source": "The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of large-scale labeled data for training.On the other hand, the trained classifiers have traditionally been evaluated on a handful of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.It is thus questionable whether recent performance improvements on the excessively re-used test sets generalize to real-world natural images with much richer content variations.In addition, studies on adversarial learning show that it is effortless to construct adversarial examples that fool nearly all image classifiers, adding more complications to relative performance comparison of existing models.This work presents an efficient framework for comparing image classifiers, which we name the MAximum Discrepancy (MAD) competition.Rather than comparing image classifiers on fixed test sets, we adaptively sample a test set from an arbitrarily large corpus of unlabeled images so as to maximize the discrepancies between the classifiers, measured by the distance over WordNet hierarchy.Human labeling on the resulting small and model-dependent image sets reveals the relative performance of the competing classifiers and provides useful insights on potential ways to improve them.We report the MAD competition results of eleven ImageNet classifiers while noting that the framework is readily extensible and cost-effective to add future classifiers into the competition.", "target": ["我们提出了一个高效和适应性的框架，用于比较图像分类器，使分类器之间的差异最大化，而不是在固定的测试集上进行比较。", "错误发现机制，通过对 \"分歧最大 \"的测试集进行采样来比较图像分类器，通过WordNet本体的语义感知距离来衡量分歧。"]}
+{"source": "Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed  perturbations which are imperceptible to humans but can cause the network to give incorrect outputs.In this paper, we design a new CNN architecture that by itself has good robustness.We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures.We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training.We next investigate the adversarial examples which “fool” a CNN with Random Mask.Surprisingly, we find that these adversarial examples often “fool” humans as well.This raises fundamental questions on how to define adversarial examples and robustness properly.", "target": ["我们提出了一种修改CNN结构的技术，以提高稳健性，同时保持较高的测试精度，并通过生成能够愚弄人类的对抗性例子，对当前对抗性例子的定义是否合适提出疑问。", "本文提出了一种简单的技术来提高神经网络对黑箱攻击的稳健性。", "作者提出了一种简单的方法来提高卷积神经网络对对抗性例子的稳健性，并取得了令人惊讶的好结果。"]}
+{"source": "Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible.There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise.Although these frameworks relax the restriction of supervised learning, they are studied independently.Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown.In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.Under this framework, we compare recent algorithms for semi-supervised and robust learning.The results suggest that semi-supervised learning outperforms robust learning with noisy labels.We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.", "target": ["我们建议在一个共享的设置下，比较半监督和稳健学习对噪声标签的影响", "作者提出了一种基于mixup的策略，用于在正式场合训练模型，其中包括半监督和稳健学习任务作为特殊情况。"]}
+{"source": "Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images.The simplest solution to solve this computationally hard problem is to decompose it into independent layerwise subproblems.However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers.In this study, a new model called Sparse Deep Predictive Coding (SDPC) is introduced to assess the impact of this inter-layer feedback connection.In particular, the SDPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of Lasso layers.A 2-layered SDPC and a Hi-La networks are trained on 3 different databases and with different sparsity parameters on each layer.First, we show that the overall prediction error generated by SDPC is lower thanks to the feedback mechanism as it transfers prediction error between layers.Second, we demonstrate that the inference stage of the SDPC is faster to converge than for the Hi-La model.Third, we show that the SDPC also accelerates the learning process.Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the SDPC features are more generic and informative.", "target": ["本文通过实验证明了分层稀疏编码算法中自上而下连接的有益效果。", "本文介绍了一项比较分层稀疏编码技术的研究，表明自上而下的项有利于减少预测误差，并且可以更快地学习。"]}
+{"source": "Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings.Recent work has primarily focused on explaining models for tasks like image classification or visual question answering.  In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification.  In this task, an explanation depends on both of the input images, so standard methods do not apply.We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match.  We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition.Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.", "target": ["解释图像相似性模型的预测的黑箱方法。", "介绍了图像相似性模型的解释方法，该方法确定了对相似性分数有积极贡献的属性，并将其与生成的突出性地图配对。", "本文提出了一种解释机制，将典型的显著图区域与属性一起配对，用于相似性匹配的深度神经网络。"]}
+{"source": "Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance.However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output.Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general.Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs.Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.", "target": ["你应该如何评估对seq2seq的对抗性攻击", "作者研究了产生对抗性例子的方法，表明用最符合引入的意义保留标准的攻击进行对抗性训练，可以提高对这种类型的攻击的稳健性，而在非对抗性设置中不会出现退化。", "本文是关于在Seq2Seq模型背景下的意义保留的对抗性扰动。"]}
+{"source": "We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs.The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium.We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.", "target": ["批量规范化的另一种规范化技术", "引入规范化技术，将卷积层的权重规范化。", "本稿介绍了一种新的层间转换，即EquiNorm，以改进批处理规范化，不修改层的输入，而是修改层的权重。"]}
+{"source": "We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a fixed length vector.In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space.This enables us to consider the problem of representation learning with a perspective from Optimal Transport and take advantage of its numerous tools such as Wasserstein distance and Wasserstein barycenters.We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance quantitatively as well as qualitatively on tasks such as measuring sentence similarity and word entailment, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec and GenSen).The key benefits of the proposed approach include:(a) capturing uncertainty and polysemy via modeling the entities as distributions,(b) utilizing the underlying geometry of the particular task (with the ground cost),(c) simultaneously providing interpretability with the notion of optimal transport between contexts and(d) easy applicability on top of existing point embedding methods.In essence, the framework can be useful for any unsupervised or supervised problem (on text or other modalities); and only requires a co-occurrence structure inherent to many problems.The code, as well as pre-built histograms, are available under https://github.com/context-mover.", "target": ["将每个实体表示为嵌入地面空间中的背景的概率分布。", "建议从背景词的直方图中构建词的嵌入，而不是作为点向量，这样可以通过一种方法来测量两个词之间的距离，即直方图之间的最佳传输，这种方法将实体的表示从标准的 \"向量空间中的点 \"增加到具有位于该向量空间中某些点的直方图。 "]}
+{"source": "Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to making small modifications of a correctly handled input.At the same time, less surprisingly, image classifiers lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise.In this work, we show that these are two manifestations of the same underlying phenomenon.We establish this connection in several ways.First, we find that adversarial examples exist at the same distance scales we would expect from a linear model with the same performance on corrupted images.Next, we show that Gaussian data augmentation during training improves robustness to small adversarial perturbations and that adversarial training improves robustness to several types of image corruptions.Finally, we present a model-independent upper bound on the distance from a corrupted image to its nearest error given test performance and show that in practice we already come close to achieving the bound, so that improving robustness further for the corrupted image distribution requires significantly reducing test error.All of this suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions.This yields a computationally tractable evaluation metric for defenses to consider: test error in noisy image distributions.", "target": ["鉴于观察到的自然数据分布之外的模型的错误率，小的对抗性扰动应该是可以预期的。", "本文通过考虑以每个测试点为中心的高斯分布中的 \"错误率\"，对高维空间中的对抗性实例提出了另一种观点。"]}
+{"source": "Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019).However, surprisingly,  the simple baseline of just pre-training and fine-tuning compact models has been overlooked.In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work.Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation.The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements.Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data.One surprising observation is that they have a compound effect even when sequentially applied on the same data.To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.", "target": ["研究自我监督学习和知识蒸馏在建立紧凑模型的背景下如何互动。", "研究通过蒸馏法训练紧凑的预训练语言模型，并表明使用教师来蒸馏紧凑的学生模型比直接预训练模型表现得更好。", "这份材料表明，直接对学生进行掩饰性语言建模的预训练比蒸馏要好，最好的是将两者结合起来，从这个预训练的学生模型中进行蒸馏。"]}
+{"source": "In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment.Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding.In particular, we examine universal randomized lattice quantization of DNNs, which randomizes DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution.Moreover, we present a method of fine-tuning vector quantized DNNs to recover the performance loss after quantization.Our experimental results show that the proposed universal DNN compression scheme compresses the 32-layer ResNet (trained on CIFAR-10) and the AlexNet (trained on ImageNet) with compression ratios of $47.1$ and $42.5$, respectively.", "target": ["我们介绍了通用的深度神经网络压缩方案，它普遍适用于任何模型的压缩，无论它们的权重分布如何，都能发挥接近最佳的效果。", "介绍了一个类似于深度压缩的网络压缩管道，使用随机晶格量化代替经典的向量化，并使用通用源编码（bzip2）代替Huffman编码。"]}
+{"source": "What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE?This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.  On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension.Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension.On disentanglement issue,   subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made.On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class.  On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors.On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.", "target": ["本文试图从理论上初步解决理想情况下的脱钩问题，并在现实情况下通过噪声建模的角度进行实践。", "研究了高斯VAE中噪声建模的重要性，并提出使用类似经验-贝叶斯的方式来训练噪声。", "修改在开发VAE模型时对噪声因素的处理方式"]}
+{"source": "Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization.Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures.We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks.", "target": ["我们研究了不同优化器的权重衰减正则化，并确定了权重衰减改善泛化的三种不同机制。", "讨论了权重衰减对深度网络模型训练的影响，包括有无批量规范化以及使用一/二阶优化方法时，并假设较大的学习率具有规范化效应。"]}
+{"source": "In this paper we present the first freely available dataset for the development and evaluation of domain adaptation methods, for the sound event detection task.The dataset contains 40 log mel-band energies extracted from $100$ different synthetic sound event tracks, with additive noise from nine different acoustic scenes (from indoor, outdoor, and vehicle environments), mixed at six different sound-to-noise ratios, SNRs, (from -12 to -27 dB with a step of -3 dB), and totaling to 5400 (9 * 100 * 6) sound files and a total length of 30 564 minutes.We provide the dataset as is, the code to re-create the dataset and remix the sound event tracks and the acoustic scenes with different SNRs, and a baseline method that tests the adaptation performance with the proposed dataset and establishes some first results.", "target": ["这���第一个免费提供的用于声音事件检测的领域适应性数据集。"]}
+{"source": "This paper aims to address the limitations of mutual information estimators based on variational optimization.By redefining the cost using generalized functions from nonextensive statistical mechanics we raise the upper bound of previous estimators and enable the control of the bias variance trade off.Variational based estimators outperform previous methods especially in high dependence high dimensional scenarios found in machine learning setups.Despite their performance, these estimators either exhibit a high variance or are upper bounded by log(batch size).Our approach inspired by nonextensive statistical mechanics uses different generalizations for the logarithm and the exponential in the partition function.This enables the estimator to capture changes in mutual information over a wider range of dimensions and correlations of the input variables whereas previous estimators saturate them.", "target": ["基于非广义统计力学的相互信息估计器", "本文试图通过引入参数q和定义q代数来建立新的相互信息的变分下限，表明该下限具有较小的方差，并实现了高值。"]}
+{"source": "Generative adversarial networks (GANs) are a widely used framework for learning generative models.Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax problem to global optimality, but in practice, are successfully trained with stochastic gradient descent-ascent.In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution in polynomial time and sample complexity.", "target": ["我们表明，随机梯度下降上升法对具有单层生成器网络的WGAN来说收敛到了全局最优。", "试图证明随机梯度下降-上升法可以收敛到WGAN的最小-最大问题的全局解。"]}
+{"source": "Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs.We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data.We find that adversarial training increases the robustness of classifiers against shared perturbations.Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations.Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs.However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring.It thus makes successful attacks on the classifier in the physical world less likely.Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.", "target": ["我们的经验表明，对抗性训练能有效地消除普遍的扰动，使对抗性例子对图像变换的稳健性降低，并使其能被检测到的方法。", "分析了对抗性训练及其对通用对抗性例子以及标准（基本迭代）对抗性例子的影响，以及对抗性训练如何影响检测。", "作者表明，对抗性训练在防止 \"共享 \"对抗性扰动，特别是防止普遍扰动方面是有效的，但在防止单一扰动方面则不太有效。"]}
+{"source": "We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.Most of the traditional approaches either manually design or use Neural Architecture Search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.Our key idea is to decouple model training from architecture search to save the cost.To this end, we propose to train a once-for-all network (OFA) that supports diverse architectural settings (depth, width, kernel size, and resolution).Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks ($> 10^{19}$) simultaneously.Extensive experiments on various hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) show that OFA consistently outperforms SOTA NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3) while reducing orders of magnitude GPU hours and $CO_2$ emission.In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting ($<$600M FLOPs).Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.", "target": ["我们介绍了训练一个适合许多硬件平台的单一的一次性网络的技术。", "该方法产生了一个网络，人们可以从该网络中提取各种资源限制（延迟、内存）的子网络，这些网络表现良好，不需要重新训练。", "本文试图用一种基于预测的NAS方法来解决为专门的资源约束的部署场景寻找最佳架构的问题。"]}
+{"source": "A deep generative model is a powerful method of learning a data distribution, which has achieved tremendous success in numerous scenarios.However, it is nontrivial for a single generative model to faithfully capture the distributions of the complex data such as images with complicate structures.In this paper, we propose a novel approach of cascaded boosting for boosting generative models, where meta-models (i.e., weak learners) are cascaded together to produce a stronger model.Any hidden variable meta-model can be leveraged as long as it can support the likelihood evaluation.We derive a decomposable variational lower bound of the boosted model, which allows each meta-model to be trained separately and greedily.We can further improve the learning power of the generative models by combing our cascaded boosting framework with the multiplicative boosting framework.", "target": ["提出一种通过级联隐性变量模型提升生成模型的方法", "本文提出了一种用于提升生成模型的级联提升的新方法，它允许每一个元模型被单独和贪婪地训练。"]}
+{"source": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks.Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline.We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena.We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.", "target": ["我们在ELMo和相关的语境嵌入模型中探究句子结构。我们发现现有的模型能有效地编码句法，并显示出长程依赖的证据，但在语义任务上只提供小的改进。", "提出了 \"边缘探测 \"方法，并关注跨度之间的关系，而不是单个词，使作者能够研究句法构成、依赖关系、实体标签和语义角色标签。", "通过编译一套 \"边缘探测 \"任务，对什么是捕获的上下文嵌入词提供了新的见解。 "]}
+{"source": "Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc.Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations.This paper presents  Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for partial and complex observations.DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which allows for reasoning with partial observations over multiple time steps.While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model that is training directly for decision making.We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, because it circumvents the difficulty of modelling observations explicitly.In most cases, DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark that we introduce.We further show that DPFRL performs well for visual navigation with real-world data.", "target": ["我们介绍了DPFRL，这是一个在部分和复杂观察下的强化学习框架，具有完全可区分的颗粒过滤器。", "介绍了训练具有潜在状态变量的DLR代理的想法，其模型为信念分布，因此它们可以处理部分观察的环境。", "本文介绍了一种用于POMDP RL的原则性方法：判别性粒子过滤器强化学习，该方法允许在多个时间步骤中对部分观测进行推理，在基准上达到最先进的水平。"]}
+{"source": "Extending models with auxiliary latent variables is a well-known technique to in-crease model expressivity.Bachman & Precup (2015); Naesseth et al. (2018); Cremer et al. (2017); Domke & Sheldon (2018) show that Importance Weighted Autoencoders (IWAE) (Burda et al., 2015) can be viewed as extending the variational family with auxiliary latent variables.Similarly, we show that this view encompasses many of the recent developments in variational bounds (Maddisonet al., 2017; Naesseth et al., 2018; Le et al., 2017; Yin & Zhou, 2018; Molchanovet al., 2018; Sobolev & Vetrov, 2018).The success of enriching the variational family with auxiliary latent variables motivates applying the same techniques to the generative model.We develop a generative model analogous to the IWAE bound and empirically show that it outperforms the recently proposed Learned Accept/Reject Sampling algorithm (Bauer & Mnih, 2018), while being substantially easier to implement.Furthermore, we show that this generative process provides new insights on ranking Noise Contrastive Estimation (Jozefowicz et al.,2016; Ma & Collins, 2018) and Contrastive Predictive Coding (Oord et al., 2018).", "target": ["使用辅助变量变分推理对蒙特卡洛目标进行了分析，得出了CPC和NCE的新分析以及新的生成模型。", "提出了关于用辅助潜变量模型改进变分界线的不同观点，并探讨了这些模型在生成模型中的使用。"]}
+{"source": "Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems.SGD estimates the gradient by uniform sampling with sample size one.There have been several other works that suggest faster epoch wise convergence by using weighted non-uniform sampling for better gradient estimates.Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient.As a result, the false impression of faster convergence in iterations leads to slower convergence in time, which we call a chicken-and-egg loop.In this paper, we break this barrier by providing the first demonstration of a sampling scheme, which leads to superior gradient estimation, while keeping the sampling cost per iteration similar to that of the uniform sampling.Such an algorithm is possible due to the sampling view of Locality Sensitive Hashing (LSH), which came to light recently.As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms.We demonstrate the benefits of our proposal on both SGD and AdaGrad.", "target": ["我们改进了所有现有梯度下降算法的运行。", "作者提出通过使用LSH从与梯度大小成比例的单调函数中抽样随机梯度。", "考虑SGD对二次损失的例子之和形式的目标。"]}
+{"source": "In recent years we have made significant progress identifying computational principles that underlie neural function.While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities.I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI.Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.", "target": ["人们普遍认识到目前人工智能的局限性，但较少有人意识到我们对大脑的了解足以立即提供新颖的人工智能方案。"]}
+{"source": "Recent work has demonstrated how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling – action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019).After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with a host of synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent.The performance of different agents when probed in this way reveals that they learn to encode detailed, and seemingly compositional, information about objects, properties and spatial relations from their physical environment.Our approach is intuitive, i.e. humans can easily interpret the responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach.By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives.", "target": ["我们用回答问题的方式来评估代理能通过自我监督的预测学习多少关于环境的知识。", "提出QA作为研究代理在世界中学习什��的工具，认为这对人类来说是一种直观的方法，允许有任意的复杂性。", "作者提出了一个框架来评估由预测模型建立的表征，这些表征包含足够的信息来回答关于它们被训练的环境的问题，显示那些由SimCore建立的表征包含足够的信息让LSTM准确回答问题。"]}
+{"source": "In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes.Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information.Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.", "target": ["我们开发了一种利用对抗性例子进行不平衡分类的新方法", "提出了一个新的优化目标，通过过度取样多数类而不是少数类来生成合成样本，解决了对少数类过度拟合的问题。", "作者提出用重新抽样的方法来解决不平衡分类的问题，表明少数类中的对抗性例子将有助于训练出一个更好地泛化的新模型。"]}
+{"source": "Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena.A model, synthetic active system composed of microtubule polymers driven by protein motors spontaneously forms a liquid-crystalline nematic phase.Extensile stress created by the protein motors precipitates continuous buckling and folding of the microtubules creating motile topological defects and turbulent fluid flows.Defect motion is determined by the rheological properties of the material; however, these remain largely unquantified.Measuring defects dynamics can yield fundamental insights into active nematics, a class of materials that include bacterial films and animal cells.Current methods for defect detection lack robustness and precision, and require fine-tuning for datasets with different visual quality.  In this study, we applied Deep Learning to train a defect detector to automatically analyze microscopy videos of the microtubule active nematic.  Experimental results indicate that our method is robust and accurate.It is expected to significantly increase the amount of video data that can be processed.", "target": ["CNN在软凝聚态物理实验中的一个有趣的应用。", "作者证明，深度学习方法可以提高识别精度和识别向列液晶缺陷的速率。", "应用一个众所周知的神经模型（YOLO）来检测图像中物体的边界盒。"]}
+{"source": "In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed.The results of our experiment show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.", "target": ["构成性和位置性对零样本学习的表征学习的影响分析。", "提出了ZSL的评估框架，其中不允许对模型进行预训练，而是随机初始化模型参数，以便更好地了解ZSL中发生的情况。"]}
+{"source": "It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples.In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear.Here we show that distributions of logit differences have a universal functional form.This functional form is independent of architecture, dataset, and training protocol; nor does it change during training.This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation.We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD).Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness.Finally, we study the effect of network architectures on adversarial sensitivity.To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10.Our resulting architecture is more robust to white \\emph{and} black box attacks compared to previous attempts.", "target": ["在所有研究的数据集和模型中，对抗性错误具有类似的幂律形式，而且架构很重要。"]}
+{"source": "Reinforcement learning (RL) has led to increasingly complex looking behavior in recent years.However, such complexity can be misleading and hides over-fitting.We find that visual representations may be a useful metric of complexity, and both correlates well objective optimization and causally effects reward optimization.We then propose curious representation learning (CRL) which allows us to use better visual representation learning algorithms to correspondingly increase visual representation in policy through an intrinsic objective on both simulated environments and transfer to real images.Finally, we show better visual representations induced by CRL allows us to obtain better performance on Atari without any reward than other curiosity objectives.", "target": ["我们提出了一个将好奇心作为视觉表征学习问题的表述，并表明它可以在代理中实现良好的视觉表征。", "本文将基于好奇心的RL训练表述为学习一个视觉表征模型，认为专注于更好的LR和最大化新奇场景的模型损失将得到更好的整体性能。"]}
+{"source": "This paper introduces the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry.This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts.This will open up new possibilities for interactions with object in a scene, for instance for virtual or robotic agents.To address this task, we propose 3D-SIC, a new data-driven approach that jointly detects object instances and predicts their completed geometry.The core idea of 3D-SIC is a novel end-to-end 3D neural network architecture that leverages joint color and geometry feature learning.The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass.In a series evaluation, we evaluate on both real and synthetic scan benchmark data, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.", "target": ["从一个场景的不完整的RGB-D扫描中，我们的目标是检测构成该场景的各个物体实例，并推断出它们完整的物体几何形状。", "提出了一个端到端的3D CNN结构，它结合了颜色特征和3D特征，从RGB-D扫描中预测场景中缺失的3D结构。", "作者提出了一个新颖的端到端三维卷积网络，它可以预测三维语义实例的完成情况，作为对象的边界框、类标签和完整的物体几何。"]}
+{"source": "Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains.We introduce XGAN (\"Cross-GAN\"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions.  We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space.We report promising qualitative results for the task of face-to-cartoon translation.The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.", "target": ["XGAN是一个无监督的模型，用于特征级的图像到图像的转换，适用于语义风格的转移问题，如脸部到卡通任务，为此我们引入了一个新的数据集。", "本文提出了一个新的基于GAN的模型，用于类似DTN非配对图像间的转换"]}
+{"source": "Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines.As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable.The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults.We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD.Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote.This algorithm uses 32x less communication per iteration than full-precision, distributed SGD.Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct.Aggregating sign gradients by majority vote means that no individual worker has too much power.We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially.The class of adversaries we consider includes as special cases those that invert or randomise their gradient estimate.On the practical side, we built our distributed training system in Pytorch.Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.", "target": ["工作者向服务器发送梯度标志，更新由多数人投票决定。我们表明，这种算法在理论上和实践上都是收敛的，通信效率高且容错。", "提出了一个以多数票为聚合的signSGD的分布式实现。"]}
+{"source": "Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drugs applied at different dosages can be quantified.The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images.An important known issue for such methods is separating relevant biological signal from nuisance variation.For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for cells from a different week, despite having identical drug compounds applied in both cases.In this case, the particular batch a set of experiments were conducted in constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g. drug effects).We develop a general framework for adjusting the image embeddings in order to `forget' domain-specific information while preserving relevant biological information.To do this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment.For the dataset presented, the replicated treatment is the negative control.We find that for our transformed embeddings (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.", "target": ["我们纠正不同领域的图像嵌入的干扰性变化，只保留相关信息。", "讨论了一种调整图像嵌入的方法，以便将技术变化与生物信号区分开来。", "作者提出了一种方法，通过训练一个最小化分布之间的Wasserstein距离的网络，在保留相关生物信息的同时去除特定领域信息。"]}
+{"source": "This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size.MINE is  back-propable and we prove that it is strongly consistent.We illustrate a handful of applications in which MINE is succesfully applied  to enhance the property of generative models in both unsupervised and supervised settings.We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems.Our results  demonstrate substantial added flexibility and improvement in these settings.", "target": ["一个在样本量和维度上可扩展的相互信息估计器。"]}
+{"source": "Reinforcement learning methods have recently achieved impressive results on a wide range of control problems.However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution.This limitation largely prohibits  their usage for complex input spaces such as video signals, and it is still impossible to use it for a number of complex problems in a real world environments, including many of those for video based control.Supervised learning, on the contrary, is capable of learning on a relatively small number of samples, however it does not take into account reward-based control policies and is not capable to provide independent control policies.  In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments.We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-the-art reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality.Additionally, we demonstrate the applicability of the method to MuJoCo control problems.", "target": ["强化学习和监督学习的新组合，极大地减少了视频训练所需的样本数量", "本文提出利用标记的受控数据来加速基于强化的控制策略的学习。"]}
+{"source": "A typical experiment to study cognitive function is to train animals to perform tasks, while the researcher records the electrical activity of the animals neurons.The main obstacle faced, when using this type of electrophysiological experiment to uncover the circuit mechanisms underlying complex behaviors, is our incomplete access to relevant circuits in the brain.One promising approach is to model neural circuits using an artificial neural network (ANN), which can provide complete access to the “neural circuits” responsible for a behavior.More recently, reinforcement learning models have been adopted to understand the functions of cortico-basal ganglia circuits as reward-based learning has been found in mammalian brain.In this paper, we propose a Biologically-plausible Actor-Critic with Episodic Memory (B-ACEM) framework to model a prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit, which is verified to capture the behavioral findings from a well-known perceptual decision-making task, i.e., random dots motion discrimination.This B-ACEM framework links neural computation to behaviors, on which we can explore how episodic memory should be considered to govern future decision.Experiments are conducted using different settings of the episodic memory and results show that all patterns of episodic memories can speed up learning.In particular, salient events are prioritized to propagate reward information and guide decisions.Our B-ACEM framework and the built-on experiments give inspirations to both designs for more standard decision-making models in biological system and a more biologically-plausible ANN.", "target": ["前额叶皮层-基底神经节-海马体（PFC-BG）回路的生物合理性框架验证了通过情景记忆的快速学习"]}
+{"source": "Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory.In a seminal paper, Telgarsky high- lighted the benefits of depth by presenting a family of functions (based on sim- ple triangular waves) for which DNNs achieve zero classification error, whereas shallow networks with fewer than exponentially many nodes incur constant error.Even though Telgarsky’s work reveals the limitations of shallow neural networks, it doesn’t inform us on why these functions are difficult to represent and in fact he states it as a tantalizing open question to characterize those functions that cannot be well-approximated by smaller depths.In this work, we point to a new connection between DNNs expressivity and Sharkovsky’s Theorem from dynamical systems, that enables us to characterize the depth-width trade-offs of ReLU networks for representing functions based on the presence of a generalized notion of fixed points, called periodic points (a fixed point is a point of period 1).Motivated by our observation that the triangle waves used in Telgarsky’s work contain points of period 3 – a period that is special in that it implies chaotic behaviour based on the celebrated result by Li-Yorke – we proceed to give general lower bounds for the width needed to represent periodic functions as a function of the depth.Technically, the crux of our approach is based on an eigenvalue analysis of the dynamical systems associated with such functions.", "target": ["在这项工作中，我们指出了DNN的表现力和来自动力系统的Sharkovsky定理之间的新联系，这使我们能够描述ReLU网络的深度-宽度权衡。", "显示了NN的表达能力如何取决于它的深度和宽度，进一步理解了深度网络对表示某些函数类别的好处。", "作者推导出了深度-宽度权衡条件，以确定relu网络何时能够使用动态系统分析来表示周期函数。"]}
+{"source": "We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS).We propose approaches to further reduce quantization bits via integrating quantization into keyword spotting model training, which we refer to as quantization-aware training.Our experimental results on large dataset indicate that quantization-aware training can recover performance models quantized to lower bits representations.By combining quantization-aware training and weight matrix factorization, we are able to significantly reduce model size and computation for small-footprint keyword spotting, while maintaining performance.", "target": ["我们研究了在非常低位的量化关键字探测仪中的量化感知训练，以降低设备上的关键字探测成本。", "本报告提出了一种结合低秩分解和量化的方法来压缩用于关键词发现的DNN模型。"]}
+{"source": "Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems.However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge.To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions.MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions.We call this dimension the Enhanced Experimental Signal (EES).MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values.This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation.We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.", "target": ["用于量化单细胞生物医学数据中实验扰动影响的新型图形信号处理框架。", "本文介绍了几种处理生物细胞实验结果的方法，并提出了一种MELD算法，将硬组分配映射为软分配，使相关的细胞组得以聚类。"]}
+{"source": "Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions.Gaussian processes (GPs), as well as nonlinear extensions thereof, provide a flexible framework to learn user models in conjunction with approximate Bayesian inference.However, the resulting models may not be interpretable in general.We propose decision-rule GPs (DRGPs) that apply GPs in a transformed space defined by decision rules that have immediate interpretability to practitioners.We illustrate this modeling tool on a real application and show that structural variational inference techniques can be used with DRGPs.We find that DRGPs outperform the direct use of GPs in terms of out-of-sample performance.", "target": ["我们提出了一类基于使用高斯过程的用户模型，适用于由决策规则定义的转换空间。"]}
+{"source": "While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multi-fidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO approaches that use only full-fidelity observations.In this paper, we propose a novel Bayesian optimization algorithm, the continuous-fidelity knowledge gradient (cfKG) method, that can be used when fidelity is controlled by one or more continuous settings such as training data size and the number of training iterations.cfKG characterizes the value of the information gained by sampling a point at a given fidelity, choosing to sample at the point and fidelity with the largest value per unit cost.Furthermore, cfKG can be generalized, following Wu et al. (2017), to settings where derivatives are available in the optimization process, e.g. large-scale kernel learning, and where more than one point can be evaluated simultaneously.Numerical experiments show that cfKG outperforms state-of-art algorithms when optimizing synthetic functions, tuning convolutional neural networks (CNNs) on CIFAR-10 and SVHN, and in large-scale kernel learning.", "target": ["我们提出了一种贝叶斯最优的贝叶斯优化算法，通过利用廉价的近似值来进行超参数的调整。", "通过贝叶斯优化研究超参数优化，使用知识梯度框架，并允许贝叶斯优化器针对成本调整保真度。"]}
+{"source": "Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence.Verification of networks enables us to gauge their vulnerability to such adversarial examples.We formulate verification of piecewise-linear neural networks as a mixed integer program.On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art.We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available.The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier.In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder.Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.", "target": ["我们用超过10万个ReLU有效地验证了深度神经模型的稳健性，比最先进的技术认证了更多的样本，并发现了比强一阶攻击更多的对抗性例子。", "对用于验证神经网络对对抗性扰动的稳健性的混合整数线性规划方法进行了仔细研究，并提出了对神经网络验证的MILP公式的三个增强。"]}
+{"source": "Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it.None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it.To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout.They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods.Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods.Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.", "target": ["一套方法用于获得任何给定模型的不确定性估计，而无需重新设计、重新训练或对其进行微调。", "描述了在训练过程中没有失真的情况下，测量任意神经网络的不确定性的几种方法。"]}
+{"source": "Capturing spatiotemporal dynamics is an essential topic in video recognition.In this paper, we present learnable higher-order operation as a generic family of building blocks for capturing higher-order correlations from high dimensional input video space.We prove that several successful architectures for visual classification tasks are in the family of higher-order neural networks, theoretical and experimental analysis demonstrates their underlying mechanism is higher-order.  On the task of video recognition, even using RGB only without fine-tuning with other video datasets, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on both Something-Something (V1 and V2) and Charades datasets.", "target": ["为情境学习提出的高阶操作", "提出了一个新的三维卷积块，它将视频输入与它的情境进行卷积，基于相关情境存在于图像对象周围的假设。"]}
+{"source": "Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures.The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data.Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule.We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule.With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data.For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.", "target": ["基于一致性的半监督学习模型不会收敛到一个单一的点，而是继续在一个平坦区域的周边探索不同的合理解决方案。权重平均化有助于提高泛化性能。", "本文提出将随机权重平均法��用于半监督学习背景下，认为半监督的MT/PI模型特别适合于SWA，并提出快速SWA以加快训练速度。"]}
+{"source": "In this paper, we find that by designing a novel loss function entitled, ''tracking loss'', Convolutional Neural Network (CNN) based object detectors can be successfully converted to well-performed visual trackers without any extra computational cost.This property is preferable to visual tracking where annotated video sequences for training are always absent, because rich features learned by detectors from still images could be utilized by dynamic trackers.It also avoids extra machinery such as feature engineering and feature aggregation proposed in previous studies.Tracking loss achieves this property by exploiting the internal structure of feature maps within the detection network and treating different feature points discriminatively.Such structure allows us to simultaneously consider discrimination quality and bounding box accuracy which is found to be crucial to the success.We also propose a network compression method to accelerate tracking speed without performance reduction.That also verifies tracking loss will remain highly effective even if the network is drastically compressed.Furthermore, if we employ a carefully designed tracking loss ensemble, the tracker would be much more robust and accurate.Evaluation results show that our trackers (including the ensemble tracker and two baseline trackers), outperform all state-of-the-art methods on VOT 2016 Challenge in terms of Expected Average Overlap (EAO) and robustness.We will make the code publicly available.", "target": ["从损失函数的角度看，我们成功地将一个流行的检测器RPN转换为一个表现良好的跟踪器。"]}
+{"source": "We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code.The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated.In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program.Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs.Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete.Specifically, the architecture (1) generates a  shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space.We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs.Our model is able to predict the exact correct repair 41% of the time with a single guess, compared to 13% accuracy for an attentional sequence-to-sequence model.", "target": ["一种用于对程序修复候选者进行评分和排名的神经结构，以便在不访问单元测试的情况下静态地进行语义程序修复。", "提出了一个由共享、专门化和竞争部分组成的神经网络架构，用于修复四种情况下的代码。"]}
+{"source": "Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier.In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a “sweet point\" in co-optimizing model accuracy, robustness, and efficiency.Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction.That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.", "target": ["是否有可能共同设计模型的准确性、稳健性和效率，以实现其三赢？是的!", "利用输入适应性的多个早期出口，用于对抗性攻击和防御领域，在不与较大容量假设冲突的情况下，降低平均推理复杂度。"]}
+{"source": "Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.Especially, little is known about how they represent language in their intermediate layers.In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text.We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.", "target": ["我们表明，在NLP任务中学习的CNN表征中的单个单元对特定的自然语言概念有选择性的响应。", "使用保留意义的自然语言的语法单位，表明在NLP任务中学习的深度CNN的单位可以作为自然语言概念检测器。"]}
+{"source": "We study the problem of building models that disentangle independent factors of variation.Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.This labeling is of particular interest as it may be readily available without annotation costs.We introduce an autoencoder model and train it through constraints on image pairs and triplets.We show the role of feature dimensionality and adversarial training theoretically and experimentally.We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used.The numerical value of a factor has different meaning in different reference frames.When the reference depends on other factors, transferring that factor becomes ambiguous.We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.", "target": ["这主要是一篇理论性的论文，描述了使用自动编码器和GAN来分解变异因素的挑战。", "本文考虑分解图像中的变异因素，表明在一般情况下，如果没有进一步的假设，人们无法区分两种不同的变异因素，并提出了一种新的AE+GAN架构来尝试分解变异因素。", "本文研究了在弱标签数据下分解独立变异因素的挑战，并引入了数据点映射的参考模糊性一词。"]}
+{"source": "In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query.Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism.This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion.The embeddings are trained with convolutional neural networks or the word2vec model.We demonstrate the performance of this model with image retrieval and text querying data sets.", "target": ["学会用几种嵌入和注意力的方式进行排序", "建议在学习排名任务中使用注意力来结合查询和搜索结果的多种输入表征。"]}
+{"source": "Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.Recent work has shown that functional diversity of neurons may be limited to that of relatively few cell types; other work has shown that incorporating constraints into artificial neural networks (ANNs) can improve their ability to mimic neural data.Here we develop an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters.The resulting models are both more predictive and more interpretable, revealing the contributions of functional cell types to neural computation and ultimately informing the design of future ANNs.", "target": ["我们开发了一种算法，将神经活动的记录作为输入，并按细胞类型返回神经元的集群和受这些集群制约的神经活动的模型。"]}
+{"source": "Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving.Here, instead, we focus on learning in the space of algorithms: we train several state-of-the-art GNN architectures to imitate individual steps of classical graph algorithms, parallel (breadth-first search, Bellman-Ford) as well as sequential (Prim's algorithm).As graph algorithms usually rely on making discrete decisions within neighbourhoods, we hypothesise that maximisation-based message passing neural networks are best-suited for such objectives, and validate this claim empirically.We also demonstrate how learning in the space of algorithms can yield new opportunities for positive transfer between tasks---showing how learning a shortest-path algorithm can be substantially improved when simultaneously learning a reachability algorithm.", "target": ["我们监督图神经网络模仿经典图形算法的中间和阶梯式输出，恢复了非常有利的见解。", "建议通过学习基元和子程序而不是最终的输出，来训练神经网络模仿图算法。"]}
+{"source": "Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics.Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world.Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.In this work, we propose a method for learning a model encoding just such a representation for task planning.We learn a neural net that encodes the k most likely outcomes from high level actions from a given world.Our approach creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future.We demonstrate this approach via application to a stacking task in a cluttered environment, where the robot must select between different colored blocks while avoiding obstacles, in order to perform a task.We also show results on a simple navigation task.Our algorithm generates realistic image and pose predictions at multiple points in a given task.", "target": ["我们描述了一个架构，用于在机器人操纵任务中为中间目标生成不同的假设。", "评估了一个拟议的生成性预测模型的质量，以生成机器人执行的计划。", "本文提出了一种学习高层次过渡函数的方法，对任务规划很有帮助。"]}
+{"source": "Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks.While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear.In this paper, we aim at bridging  this gap from both theoretical and empirical perspectives.First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem and establish $O(\\epsilon^{-4})$ complexity for finding $\\epsilon$-first-order stationary point, in which the algorithm only requires invoking one stochastic first-order oracle while enjoying state-of-the-art iteration complexity achieved by stochastic extragradient method by~\\citep{iusem2017extragradient}.Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \\emph{improved} adaptive complexity $\\widetilde{O}\\left(\\epsilon^{-\\frac{2}{1-\\alpha}}\\right)$~\\footnote{Here $\\widetilde{O}(\\cdot)$ compresses a logarithmic factor of $\\epsilon$.}, where $\\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\\leq \\alpha\\leq 1/2$.To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization.Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training.Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.", "target": ["本文对自适应梯度算法作为GAN解决非凸非凹最小值问题进行了新的分析，并通过实证研究解释了自适应梯度方法优于其非自适应同行的原因。", "开发了在随机环境下解决变分不等式的算法，提出了外梯度方法的变体。"]}
+{"source": "We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object.The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations.We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories.We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops.We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters.Training is performed purely end-to-end on the unmodified evidence lower bound objective.This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.", "target": ["我们通过使用带有高斯过程先验的VAE结构，只从一个玩具视频数据集的像素中学习物体的精确轨迹。"]}
+{"source": "Dreams and our ability to recall them are among the most puzzling questions in sleep research.Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood.In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.).Our model achieves significant accuracy levels across all the sleep stages, thereby indicating subtle signatures of dream recall in the sleep microstructure.We also visualized the feature space to inspect the subject-specificity of the learned features, thus ensuring that the network captured population level differences.Beyond being the first study to apply deep learning to sleep EEG in order to classify HDR and LDR, guided backpropagation allowed us to visualize the most discriminant features in each sleep stage.The significance of these findings and future directions are discussed.", "target": ["我们使用卷积神经网络和特征可视化技术，如tSNE和引导-反向传播，研究梦境回忆的神经基础。"]}
+{"source": "This paper considers multi-agent reinforcement learning (MARL) in networked system control.Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors.We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent.Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL.Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.", "target": ["本文提出了网络化多代理控制问题的新公式和新通信协议", "关注N-MARL's，其中代理只根据来自邻近节点的消息更新他们的策略，表明引入空间折扣因素可以稳定学习。"]}
+{"source": "Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance.In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization.For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence.This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models' performance.Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.", "target": ["均值字段VB使用了两倍的参数；我们在均值字段VB中绑定了方差参数，而在ELBO中没有任何损失，获得了速度和更低的方差梯度。"]}
+{"source": "Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining.Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest.In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available.Our main contributions are as follows.First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier.  Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student).Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words.Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.", "target": ["我们有效地利用一些关键词作为弱监督来训练神经网络的方面提取。", "讨论了一种知识蒸馏的变体，它使用基于种子词的词包分类器的 \"教师 \"和基于嵌入的神经网络的 \"学生\"。"]}
+{"source": "Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons.However, the relative contributions of these connections to perceptual grouping are poorly understood.We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level \"Gestalt\" vs. high-level object cues for perceptual grouping.We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing.Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities, whereas top-down connections rescue learning on tasks with high-level object cues by modifying coarse predictions about the position of the target object.Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.", "target": ["水平和自上而下的反馈连接是生物和循环视觉系统中互补的知觉分组策略的原因。", "利用神经网络作为大脑的计算模型，研究了解决两个视觉挑战的不同策略的效率。"]}
+{"source": "Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images.However, their application in the audio domain has received limited attention,and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech.Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes.The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced.  To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Fréchet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS.We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator.Listen to GAN-TTS reading this abstract at http://tiny.cc/gantts.", "target": ["我们介绍了GAN-TTS，一个用于文本到语音的生成对抗网络，它达到了平均意见得分（MOS）4.2。", "解决了原始波形合成中的GAN挑战，并开始缩小自回归模型和GAN在原始音频中的现有性能差距。"]}
+{"source": "This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.Different from existing works, our PiT framework employs the sparse penalties to train networks and thus help rank the importance of weights and filters.Our PiT algorithms can directly prune the network without any fine-tuning.The pruned networks can still achieve comparable performance to the original networks.In particular, we introduce the (Group) Lasso-type Penalty (L-P /GL-P), and (Group) Split LBI Penalty (S-P / GS-P) to regularize the networks, and a pruning strategy proposed  is used in help prune the network.We conduct the extensive experiments on MNIST, Cifar-10, and miniImageNet.The results validate the efficacy of our proposed methods.Remarkably, on MNIST dataset, our PiT framework can save 17.5% parameter size of LeNet-5, which achieves the 98.47% recognition accuracy.", "target": ["我��提出了一种通过强制执行结构稀疏度惩罚来学习修剪网络的算法。", "本文介绍了一种在训练网络时使用套索和分割LBI惩罚的修剪方法。"]}
+{"source": "We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.Given limited labeled data just before inference, those representations can also be associated with specific object types to perform classification.To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM), which loosely models the function of a cortical column in the mammalian brain.Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns and forgetting outliers, and top-down predictions.We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examples per class.STAMs suggest a promising direction to solve the UCL problem without catastrophic forgetting.", "target": ["我们介绍了无监督持续学习（UCL）和一个解决UCL问题的神经启发架构。", "建议使用STAM模块的层次来解决UCL问题，提供证据表明模块所学的表征非常适合于少样本分类。"]}
+{"source": "Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems.Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation.We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters.Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect.", "target": ["傅里叶域中的新信号提取方法", "贡献了一个复值卷积版本的逐特征线性调制，它允许参数优化并设计了一个考虑到幅度和相位的损失。"]}
+{"source": "It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation in a different and unpredictable way.It is rare for one to have access to a large number of data to help separate the influences.In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner.We address this problem in a two-branch Autoencoder framework.For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge.This encourages the branch to distill geometry information.Another branch learns the complementary style information.The two branches form an effective framework that can disentangle object's content-style representation without any human annotation.We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data.We are able to generate photo-realistic images with 256x256 resolution that are clearly disentangled in content and style.", "target": ["我们提出了一个新的框架，以完全无监督的方式学习内容和风格的分离表示。", "提出了基于自动编码器框架的模型，以分解对象的表征，结果显示，该模型可以产生捕捉内容和风格的表征。"]}
+{"source": "We develop the Y-learner for estimating heterogeneous treatment effects in experimental and observational studies.The Y-learner is designed to leverage the abilities of neural networks to optimize multiple objectives and continually update, which allows for better pooling of underlying feature information between treatment and control groups.We evaluate the Y-learner on three test problems: (1) A set of six simulated data benchmarks from the literature.(2) A real-world large-scale experiment on voter persuasion.(3) A task from the literature that estimates artificially generated treatment effects on MNIST didgits.The Y-learner achieves state of the art results on two of the three tasks.On the MNIST task, it gets the second best results.", "target": ["我们开发了一种CATE估计策略，利用了神经网络的一些有趣的特性。", "通过对治疗响应函数、控制响应函数以及从估算的治疗效果到条件平均治疗效果的映射进行建模，显示了对X-learner的改进，作为神经网络。", "作者提出用Y-learner来估计条件平均治疗效果（CATE），它同时更新结果函数的参数和CATE估计器。"]}
+{"source": "With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems.Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware.In recent years, we have witnessed devastating security breaches targeting IoT devices.These security concerns have significantly hamstrung further evolution of IoT technology.In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices.We do not encode into our emulator any specific information about a device.Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly.This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device.To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator.We successfully identified both self-injected and real-world vulnerabilities.", "target": ["与设备无关的固件执行"]}
+{"source": "Deep neural models, such as convolutional and recurrent networks, achieve phenomenal results over spatial data such as images and text.However, when considering tabular data, gradient boosting of decision trees (GBDT) remains the method of choice.Aiming to bridge this gap, we propose \\emph{deep neural forests} (DNF)--  a novel architecture that combines elements from decision trees as well as dense residual connections. We present the results of extensive empirical study in which we examine the performance of GBDTs, DNFs and (deep) fully-connected networks. These results indicate that DNFs achieve comparable results to GBDTs on tabular data, and open the door to end-to-end neural modeling of multi-modal data.To this end, we present a successful application of DNFs as part of a hybrid architecture for a multi-modal driving scene understanding classification task.", "target": ["一个用于表格数据的架构，它模拟了决策树的分支，并使用密集的残差连接性", "本文提出了深度神经森林，这是一种针对表格数据的算法，整合了决策树梯度提升的强项。", "一种模仿决策森林工作方式的新型神经网络架构，解决了为表格数据训练深度模型的一般问题，并展示了与GBDT相同的有效性。"]}
+{"source": "Hyperparameter tuning is one of the most time-consuming workloads in deep learning.State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable.Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better results.Motivated by this trend, we ask: can simple adaptive methods, based on SGD perform as well or better?We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.We then analyze its robustness to learning rate misspecification and objective curvature variation.Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD.YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly.We empirically show YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to $3.28$x in synchronous and up to $2.69$x in asynchronous settings.", "target": ["YellowFin是一个基于SGD的优化器，具有动量和学习率的适应性。", "提出了一种在动量SGD方法中自动调整动量参数的方法，该方法取得了比最先进的Adam算法更好的结果和快速收敛速度。"]}
+{"source": "Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits.With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms.Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack.This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.", "target": ["对变分自动编码器的潜在空间进行对抗性攻击以改变输入的语义", "本文涉及安全和机器学习，提出了一种中间人攻击，改变了输入数据的VAE编码，使解码后的输出会被错误分类。"]}
+{"source": "Graph-based dependency parsing consists of two steps: first, an encoder produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a decoder} finds the parse tree whose substructures have the largest total score.Over the past few years, powerful neural techniques have been introduced into the encoding step which substantially increases parsing accuracies.However, advanced decoding techniques, in particular high-order decoding, have seen a decline in usage.It is widely believed that contextualized features produced by neural encoders can help capture high-order decoding information and hence diminish the need for a high-order decoder.In this paper, we empirically evaluate the combinations of different neural and non-neural encoders with first- and second-order decoders and provide a comprehensive analysis about the effectiveness of these combinations with varied training data sizes.We find that: first, when there is large training data, a strong neural encoder with first-order decoding is sufficient to achieve high parsing accuracy and only slightly lags behind the combination of neural encoding and second-order decoding; second, with small training data, a non-neural encoder with a second-order decoder outperforms the other combinations in most cases.", "target": ["检验不同编码器-解码器组合对依赖性解析任务的有效性的实证研究", "实证性地分析了各种编码器、解码器以及它们对基于图的依赖性解析的依赖性。"]}
+{"source": "Meta-learning will be crucial to creating lifelong, generalizable AI.In practice, however, it is hard to define the meta-training task distribution that is used to train meta-learners.If made too small, tasks are too similar for a model to meaningfully generalize.If made too large, generalization becomes incredibly difficult.We argue that both problems can be alleviated by introducing a teacher model that controls the sequence of tasks that a meta-learner is trained on.This teacher model is incentivized to start the student meta-learner on simple tasks then adaptively increase task difficulty in response to student progress.While this approach has been previously studied in curriculum generation, our main contribution is in extending it to meta-learning.", "target": ["像人类一样训练元学习者的教师"]}
+{"source": "Using higher order knowledge to reduce training data has become a popular research topic.However, the ability for available methods to draw effective decision boundaries is still limited: when training set is small, neural networks will be biased to certain labels.Based on this observation, we consider constraining output probability distribution as higher order domain knowledge.We design a novel algorithm that jointly optimizes output probability distribution on a clustered embedding space to make neural networks draw effective decision boundaries.  While directly applying probability constraint is not effective, users need to provide additional very weak supervisions: mark some batches that have output distribution greatly differ from target probability distribution.We use experiments to empirically prove that our model can converge to an accuracy higher than other state-of-art semi-supervised learning models with less high quality labeled training examples.", "target": ["我们引入了一种嵌入空间的方法来约束神经网络的输出概率分布。", "本文介绍了一种用深度神经网络进行半监督学习的方法，在训练规模较小的情况下，该模型取得了相对较高的准确性。", "本文在训练实例数量有限的情况下，将标签分布纳入模型学习，并提出了两种处理输出标签分布错误偏差问题的技术。"]}
+{"source": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy).  Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus.We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis.  We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.", "target": ["我们介绍了一种新型的深度语境化词汇表征，在一系列具有挑战性的NLP任务中显著提高了技术水平。"]}
+{"source": "This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data.We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning.Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead.Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences.We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.", "target": ["这项工作引入了一个新的损失函数，用于在标签不一致的情况下对时间定位DNN进行稳健性训练。", "一种新的损失，用于训练模型，通过比较平滑的标签和预测序列，预测事件在具有噪声标签的训练序列中的发生位置。"]}
+{"source": "The driving force behind deep networks is their ability to compactly represent rich classes of functions.The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another.To date, expressive efficiency analyses focused on the architectural feature of depth, showing that deep networks are representationally superior to shallow ones.In this paper we study the expressive efficiency brought forth by connectivity, motivated by the observation that modern networks interconnect their layers in elaborate ways.We focus on dilated convolutional networks, a family of deep models delivering state of the art performance in sequence processing tasks.By introducing and analyzing the concept of mixed tensor decompositions, we prove that interconnecting dilated convolutional networks can lead to expressive efficiency.In particular, we show that even a single connection between intermediate layers can already lead to an almost quadratic gap, which in large-scale settings typically makes the difference between a model that is practical and one that is not.Empirical evaluation demonstrates how the expressive efficiency of connectivity, similarly to that of depth, translates into gains in accuracy.This leads us to believe that expressive efficiency may serve a key role in developing new tools for deep network design.", "target": ["我们介绍了混合张量分解的概念，并利用它来证明扩张卷积网络的互连能提高其表达能力。", "本文从理论上验证了使用混合张量分解，将不同扩张的网络相互连接可以带来表达效率。", "作者研究了扩张卷积网络，并表明在不同阶段交织两个扩张卷积网络A和B，比不交织更有表达效率。", "表明WaveNet的单一完美二叉树的结构假设阻碍了它的性能，具有更复杂的混合树结构的类似WaveNet的架构表现更好。"]}
+{"source": "We apply multi-task learning to image classification tasks on MNIST-like datasets.MNIST dataset has been referred to as the {\\em drosophila} of machine learning and has been the testbed of many learning theories.The NotMNIST dataset and the FashionMNIST dataset have been created with the MNIST dataset as reference.In this work, we exploit these MNIST-like datasets for multi-task learning.The datasets are pooled together for learning the parameters of joint classification networks.Then the learned parameters are used as the initial parameters to retrain disjoint classification networks.The baseline recognition model are all-convolution neural networks.Without multi-task learning, the recognition accuracies for MNIST, NotMNIST and FashionMNIST are 99.56\\%, 97.22\\% and 94.32\\% respectively.With multi-task learning to pre-train the networks, the recognition accuracies are respectively 99.70\\%, 97.46\\% and 95.25\\%.The results re-affirm that multi-task learning framework, even with data with different genres, does lead to significant improvement.", "target": ["多任务学习的作用", "本文介绍了一种用于类似MNIST数据集分类的多任务神经网络"]}
+{"source": "Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network.To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization.This approach provides us with a broad and unifying view on much prior work on this topic.Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.In particular, they specify a concrete security guarantee that would protect against a well-defined class of adversaries.These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks.They also suggest robustness against a first-order adversary as a natural security guarantee.We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.", "target": ["我们对对抗性例子的概念进行了原则性的、基于优化的重新审视，并开发了产生对抗性模型的方法，这些模型对广泛的对抗者具有对抗性的稳健性。", "研究深度网络学习的最小最大公式，以提高其稳健性，使用投影梯度下降作为主要对手。", "本文拟通过鞍点问题的框架，研究如何使神经网络抵抗对抗性损失。"]}
+{"source": "In recent years there has been a rapid increase in classification methods on graph structured data.Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance.However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set.This prevents fair competition of the algorithms and raises a question of the validity of the obtained results.We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.", "target": ["许多图分类数据集都有重复的数据，因此提出了关于泛化能力和模型公平比较的疑问。", "作者讨论了图数据集中的同构偏差，即当图同构特征被纳入模型时，学习网络的过拟合效应，理论上类似于数据泄露效应。"]}
+{"source": "Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.However, learning from demonstrations often suffers from the covariate shift problem, which resultsin cascading errors of the learned policy.We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction.We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation.We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks.We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.", "target": ["我们引入了一个保守外推值函数的概念，它可以证明导致策略可以自我修正以保持接近示范状态，并通过一个新的负采样技术学习它们。", "一种被称为负采样的价值迭代的算法，以解决模仿学习中的协变量转移问题。"]}
+{"source": "A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition.Learning such a structured world model from raw sensory data remains a challenge.As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs).C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure.We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network.This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process.We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation.Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.", "target": ["对比训练的结构化世界模型（C-SWMs）从原始像素输入中学习面向对象的状态表示和环境的关系模型。", "作者通过使用一个对比性的潜在空间，克服了在构建和学习结构化世界模型时使用基于像素的损失的问题。"]}
+{"source": "Neural machine translation (NMT) models learn representations containing substantial linguistic information.However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons.We develop unsupervised methods for discovering important neurons in NMT models.Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision.We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena.Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.", "target": ["寻找、分析和控制NMT中重要神经元的无监督方法", "这项工作提出在神经机器翻译模型中寻找 \"有意义的 \"神经元，方法是根据模型对、不同历时或不同数据集之间的相关性进行排名，并提出模型的控制机制。"]}
+{"source": "Computations for the softmax function in neural network models are expensive when the number of output classes is large.This can become a significant issue in both training and inference for such models.In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.During inference, our method quickly locates the most probable expert to compute small-scale softmax.Our method is learning-based and requires no knowledge of the output class partition space a priori.We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of", "target": ["我们提出了双重稀疏softmax，即稀疏专家的稀疏混合物，通过利用两级重叠的层次结构来提高softmax推理的效率。", "本文提出了新的Softmax算法实现，该算法具有两个层次的稀疏性，加快了语言建模的操作。"]}
+{"source": "Our work presents empirical evidence that layer rotation, i.e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance.Compared to previously studied indicators of generalization, we show that layer rotation has the additional benefit of being easily monitored and controlled, as well as having a network-independent optimum: the training procedures during which all layers' weights reach a cosine distance of 1 from their initialization consistently outperform other configurations -by up to 20% test accuracy.Finally, our results also suggest that the study of layer rotation can provide a unified framework to explain the impact of weight decay and adaptive gradient methods on generalization.", "target": ["本文提出了支持发现泛化指标的实证证据：每个层的权重向量与其初始化之间的余弦距离在整个训练中的演变。"]}
+{"source": "Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program.Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths therein (e.g. data-flow relations), which are precise and abundantly available for code.This provides a strong inductive bias towards semantically meaningful relations, yielding more generalizable representations than classical sequence-based models.Unfortunately, these models primarily rely on graph-based message passing to represent relations in code, which makes them de facto local due to the high cost of message-passing steps, quite in contrast to modern, global sequence-based models, such as the Transformer.In this work, we bridge this divide between global and structured models by introducing two new hybrid model families that are both global and incorporate structural bias: Graph Sandwiches, which wrap traditional (gated) graph message-passing layers in sequential message-passing layers; and Graph Relational Embedding Attention Transformers (GREAT for short), which bias traditional Transformers with relational information from graph edge types.By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.Starting with a  graph-based model that already improves upon the prior state-of-the-art for this task by 20%, we show that our proposed hybrid models improve an additional 10-15%, while training both faster and using fewer parameters.", "target": ["结合全局和结构特征的源代码模型可以学习到更强大的程序表征。", "一种新的方法，使用像[RNN GNN RNN]这样的三明治模型来为修复错误的任务建立源代码模型，这可以显著提高定位和修复的准确性。"]}
+{"source": "Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate.While a number of works attempt to mitigate this effect through gated recurrent units, skip-connections, parametric constraints and design choices, we propose a novel incremental RNN (iRNN), where hidden state vectors keep track of incremental changes, and as such approximate state-vector increments of Rosenblatt's (1962) continuous-time RNNs.iRNN exhibits identity gradients and is able to account for long-term dependencies (LTD).We show that our method is computationally efficient overcoming overheads of many existing methods that attempt to improve RNN training, while suffering no performance degradation.We demonstrate the utility of our approach with extensive experiments and show competitive performance against standard LSTMs on LTD and other non-LTD tasks.", "target": ["增量式RNN通过基于先前的状态和ODE预测的状态之间的差异更新状态向量来解决梯度爆炸/消失问题。", "作者通过建立一个信号转换的吸引子系统并检查它是否收敛到一个平衡点来解决递归神经网络中的信号传播问题。"]}
+{"source": "Recent empirical results on over-parameterized deep networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks.Researchers are actively working on bridging this discrepancy by proposing better complexity measures.Instead, we directly measure prediction bias and variance for four classification and regression tasks on modern deep networks.We find that both bias and variance can decrease as the number of parameters grows.Qualitatively, the phenomenon persists over a number of gradient-based optimizers.To better understand the role of optimization, we decompose the total variance into variance due to training set sampling and variance due to initialization.Variance due to initialization is significant in the under-parameterized regime.In the over-parameterized regime, total variance is much lower and dominated by variance due to sampling.We provide theoretical analysis in a simplified setting that is consistent with our empirical findings.", "target": ["我们提供证据反对关于偏差-方差权衡的经典说法，并提出了一个新的方差分解。"]}
+{"source": "Real world images often contain large amounts of private / sensitive information that should be carefully protected without reducing their utilities.In this paper, we propose a privacy-preserving deep learning framework with a learnable ob- fuscator for the image classification task.Our framework consists of three mod- els: learnable obfuscator, classifier and reconstructor.The learnable obfuscator is used to remove the sensitive information in the images and extract the feature maps from them.The reconstructor plays the role as an attacker, which tries to recover the image from the feature maps extracted by the obfuscator.In order to best protect users’ privacy in images, we design an adversarial training methodol- ogy for our framework to optimize the obfuscator.Through extensive evaluations on real world datasets, both the numerical metrics and the visualization results demonstrate that our framework is qualified to protect users’ privacy and achieve a relatively high accuracy on the image classification task.", "target": ["我们提出了一个新颖的深度学习图像分类框架，既能对图像进行准确分类，又能保护用户的隐私。", "本文提出了一个框架，它保留了图像中的私人信息，并且不影响图像的可用性。", "目前的这项工作建议使用对抗性网络来混淆图像，从而允许在没有隐私顾虑的情况下收集图像，将其用于训练机器学习模型。"]}
+{"source": "Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority.All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security it’s original structure does not allow for direct analysis of address transactions. Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate.We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms.We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.", "target": ["加密货币交易图的2vec模型", "本文提出使用自动编码器、networkX和node2Vec来预测一个比特币地址是否会在一年后变成空地址，但结果比现有的基线要差。"]}
+{"source": "Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poorly understood.In this work, we analyze last-iterate convergence of simultaneous gradient descent (simGD) and its variants under the assumption of convex-concavity, guided by a continuous-time analysis with differential equations.First, we show that simGD, as is, converges with stochastic sub-gradients under strict convexity in the primal variable.Second, we generalize optimistic simGD to accommodate an optimism rate separate from the learning rate and show its convergence with full gradients.Finally, we present anchored simGD, a new method, and show convergence with stochastic subgradients.", "target": ["随机子梯度法的收敛性证明以及凸凹最小最大问题的变化", "在最小凸凹博弈的背景下，对同步随机亚梯度、同步乐观梯度和同步锚定梯度进行了分析。", "本文分析了随机梯度下降在应用于凸凹博弈时的动态变化，以及乐观主义的GD和一种新的锚定GD算法，该算法在比SGD或乐观主义的SGD更弱的假设下收敛。"]}
+{"source": "Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice.When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage.In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation.The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution.The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest.In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control.We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering.We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods.The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.", "target": ["我们提出了一个算法框架，以安排具有3-DOF重新定向能力的小型航天器星座，并与卫星间的链接联网。", "本文提出了一个优化航天器星座问题的通信模块，并比较了分布式和集中式设置中的算法。"]}
+{"source": "Importance sampling (IS) is a standard Monte Carlo (MC) tool to compute information about random variables such as moments or quantiles with unknown distributions.  IS is asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity.However, retaining infinitely many particles is intractable.We propose a scheme for only keeping a \\emph{finite representative subset} of particles and their augmented importance weights that is \\emph{nearly consistent}. To do so in {an online manner}, we approximate importance sampling in two ways.  First, we replace the deltas by kernels, yielding kernel density estimates (KDEs).  Second, we sequentially project KDEs onto nearby lower-dimensional subspaces.We characterize the asymptotic bias of this scheme as determined by a compression parameter and kernel bandwidth, which yields a tunable tradeoff between consistency and memory.In experiments,  we observe a favorable tradeoff between memory and accuracy, providing for the first time near-consistent compressions of arbitrary posterior distributions.", "target": ["我们提出了一种新型的压缩核化重要性采样算法。"]}
+{"source": "We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator?(2) how to correctly use cross-validation to choose the regularization parameter?and (3) how to accelerate computation without losing too much accuracy?We consider the three problems in a unified large-data linear model.We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise. We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction.We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate.Our results are illustrated by simulations and by analyzing empirical data.", "target": ["我们在高维渐进框架中研究了脊回归的结构，并得到了关于交叉验证和草图的见解。", "利用脊回归估计器的新渐进特征对脊回归进行理论研究。"]}
+{"source": "Attention mechanisms have advanced the state of the art in several machine learning tasks.Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness.In this paper, we address this problem by studying the landscape of population and empirical loss functions of attention-based neural networks.Our results show that, under mild assumptions, every local minimum of a two-layer global attention model has low prediction error, and attention models require lower sample complexity than models not employing attention.We then extend our analyses to the popular self-attention model, proving that they deliver consistent predictions with a more expressive class of functions.Additionally, our theoretical results provide several guidelines for designing attention mechanisms.Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.", "target": ["我们分析了有注意力的神经网络的损失情况，并解释了为什么注意力有助于训练神经网络以获得良好的性能。", "本文从理论角度证明了注意力网络在单层设置中，对于固定注意力（单层和多层）和自我注意力的泛化能力比非注意力基线更好。"]}
+{"source": "Recent advances in deep learning techniques has shown the usefulness of the deep neural networks in extracting features required to perform the task at hand.However, these features learnt are in particular helpful only for the initial task.This is due to the fact that the features learnt are very task specific and does not capture the most general and task agnostic features of the input.In fact the way humans are seen to learn is by disentangling features which task agnostic.This indicates that leaning task agnostic features by disentangling only the most informative features from the input data.Recently Variational Auto-Encoders (VAEs) have shown to be the de-facto models to capture the latent variables in a generative sense.As these latent features can be represented as continuous and/or discrete variables, this indicates us to use VAE with a mixture of continuous and discrete variables for the latent space.We achieve this by performing our experiments using a modified version of joint-vae to learn the disentangled features.", "target": ["神经分离的混合模型"]}
+{"source": "To improve how neural networks function it is crucial to understand their learning process.The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task.However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions.In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression.In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions.Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions.With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions.We also find that there is a large amount of variation in compression between different network initializations.Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting.Finally, we show that only compression of the last layer is positively correlated with generalization.", "target": ["我们为DNN开发了稳健的互信息估计，并利用它们观察具有非饱和激活函数的网络中的压缩情况", "本文研究了深度神经网络为监督任务做信息压缩的普遍观点", "本文提出了一种估计具有无界激活函数的网络的相互信息的方法，并使用L2正则化来引起更多的压缩。"]}
+{"source": "In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation.We introduce TimbreTron, a method for musical timbre transfer which applies “image” domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer.We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance.Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.We made an accompanying demo video here: https://www.cs.toronto.edu/~huang/TimbreTron/index.html which we strongly encourage you to watch before reading the paper.", "target": ["我们提出了TimbreTron，这是一个使用CQT域式传输在音乐波形上完成高质量音色传输的管道。", "通过应用为图像风格转移而开发的CycleGAN，将特定乐器的录音转换为另一种乐器的方法，用于转移谱图。", "作者使用多种技术/工具，在没有配对训练实例的情况下，实现了神经性音色转换（将音乐从一种乐器转换到另一种乐器）。", "描述了一个音乐音色传递的模型，结果表明，所提出的系统对音高和节奏的传递以及音色的适应是有效的。"]}
+{"source": "Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.But also generic hardware and software implementations of deep learning run more efficiently for sparse networks.Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints.We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network.DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded.We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance.DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.", "target": ["本文介绍了Deep Rewiring，一种可用于训练深度神经网络的算法，当网络连接性在训练过程中受到严重制约时，该算法可用于训练深度神经网络。", "一种直接在稀疏连接图上实现深度学习的方法，允许网络有效地在线训练，并实现快速和灵活的学习。", "作者提供了一个简单的算法，能够用有限的内存进行训练"]}
+{"source": "Deep learning's success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters.These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements.Some model compression methods have been successfully applied on image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks.In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods.We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator.We show that this framework has a compelling performance to high degrees of sparsity, generalizes well to new tasks and models, and enables meaningful comparisons between different pruning granularities.", "target": ["现有的修剪方法在应用于处理复杂任务的GAN时失败了，因此我们提出了一种简单而稳健的修剪生成器的方法，对各种网络和任务都很有效。", "作者针对压缩网络的任务提出了对经典蒸馏法的修改，以解决以前的解决方案在应用于生成式对抗网络时的失败。"]}
+{"source": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth.To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training.We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus.On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB.Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.", "target": ["我们发现分布式SGD中99.9%的梯度交换是多余的；我们将通信带宽减少了两个数量级，而不会失去准确性。", "本文提出了比梯度下降更多的改进，以提高通信效率"]}
+{"source": "Image-to-image translation has recently received significant attention due to advances in deep learning.Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way.However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations.To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain.Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain.To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.", "target": ["我们提出了典范引导和语义一致的图像-图像转换（EGSC-IT）网络，它将转换过程置于目标领域的典范图像上。", "讨论了I2I转换模型的一个核心故障和需求。", "本文探讨了一个图像有两个组成部分的想法，并应用了一个注意力模型，其中引导转换过程的特征掩码不需要语义标签。"]}
+{"source": "Deep neural networks can learn meaningful representations of data.However, these representations are hard to interpret.For example, visualizing a latent layer is generally only possible for at most three dimensions.Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer.Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions.To do so, we propose a class of regularizations we call \\textit{Graph Spectral Regularizations} that impose graph-structure on latent layers.This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters.This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data.First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets.Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation.In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations.Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.", "target": ["在神经网络层上强加图形结构以提高视觉可解释性。", "一种新型的正则器，将图结构强加于神经网络的隐藏层，以提高隐藏表征的可解释性。", "突出了图谱正则器对神经网络的可解释性的贡献。"]}
+{"source": "Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.The dominant parametric approach is based on locally normalized models which predict one word at a time.While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process.In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation.Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa.Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines.Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.", "target": ["我们��明，在自动回归语言模型的残差上进行训练时，基于能量的模型可以有效地用于生成文本。", "提出了一种用于文本生成的基于残余能量的模型（EBM），该模型在句子层面上运作，因此可以利用BERT，并实现较低的困惑度，而且被人类评价所青睐。"]}
+{"source": "We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale.As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models.It does not, however, improve the robustness against more natural, and typically larger, perturbations.Learning more robust features during training appears to be necessary for robustness in this second sense.We show that features derived from a model that was encouraged to learn global, shape-based representations (Geirhos et al., 2019) do not only improve the robustness against natural perturbations, but when used in conjunction with an episodic memory, they also provide additional robustness against adversarial perturbations.Finally, we address three important design choices for the episodic memory: memory size, dimensionality of the memories and the retrieval method.We show that to make the episodic memory more compact, it is preferable to reduce the number of memories by clustering them, instead of reducing their dimensionality.", "target": ["对基于大规模缓存的图像识别模型进行系统研究，特别关注其稳健性特性", "本文提出使用内存缓存来提高对对抗性图像实例的稳健性，并得出结论，使用大型连续缓存并不优于硬注意力。"]}
+{"source": "Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups.Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory.Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory).In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups.In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra.This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions.The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important.In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.", "target": ["本文描述了一个灵活的框架，用于构建对一大类变换组具有等效性的CNN。", "一个用任意Lie群G构建群组CNN的框架，它在肿瘤分类和地标定位方面显示出比CNN更优越的性能。"]}
+{"source": "Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization.Although alternative pooling methods exist (eg. max, lp norm, stochastic), the averaging operation is still the dominating global pooling scheme in popular models.As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy?We first ask: ``is there a difference between features learned by global average and max pooling?'' Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales.We then ask ``is there a single global feature pooling variant that's most suitable for fine-grained recognition?'' A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized pooling's performance increases almost monotonically as it changes from average to max.We finally ask: ``what's the best way to combine two heterogeneous pooling schemes?'' Common strategies struggle because of potential gradient conflict but the ``freeze-and-train'' trick works best.We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.", "target": ["对九个有代表性的全局池计划的基准测试显示了一些有趣的发现。", "对于细粒度的分类任务，本文验证了maxpooling会鼓励比avgpooling更稀疏的特征图，并且优于avgpooling。"]}
+{"source": "We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions.Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown.We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets.The improvements are greater when the training set is smaller or the task is more challenging.Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL.Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements.We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.", "target": ["自我监督改善了小型和具有挑战性的数据集上的几张照片的识别，而不依赖额外的数据；额外的数据只有在来自相同或类似领域时才有帮助。", "对不同的自我监督学习（SSL）方法的实证研究，显示SSL在数据集较难时帮助更大，领域对训练很重要，还有一种从无标签的数据集中选择样本的方法。"]}
+{"source": "Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy.In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces.It is based on MDP homomorphisms, a structure-preserving mapping between MDPs.We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters.Our novel task transfer method beats a baseline based on a deep Q-network.", "target": ["我们从经验中创造出抽象的环境模型，并利用它们更快地学习新的任务。", "一种利用MDP同构的思想将具有连续状态空间的复杂MDP转化为较简单的方法。"]}
+{"source": "A number of recent methods to understand neural networks have focused on quantifying the role of individual features.  One such method, NetDissect identifies interpretable features of a model using the Broden dataset of visual semantic labels (colors, materials, textures, objects and scenes).  Given the recent rise of a number of action recognition datasets, we propose extending the Broden dataset to include actions to better analyze learned action models.  We describe the annotation process, results from interpreting action recognition models on the extended Broden dataset and examine interpretable feature paths to help us understand the conceptual hierarchy used to classify an action.", "target": ["我们将网络剖析扩展到包括行动解释，并检查可解释的特征路径，以了解用于分类行动的概念层次。"]}
+{"source": "Automatic melody generation for pop music has been a long-time aspiration forboth AI researchers and musicians.However, learning to generate euphoniousmelody has turned out to be highly challenging due to a number of factors.Representationof multivariate property of notes has been one of the primary challenges.It is also difficult to remain in the permissible spectrum of musical variety, outsideof which would be perceived as a plain random play without auditory pleasantness.Observing the conventional structure of pop music poses further challenges.In this paper, we propose to represent each note and its properties as a unique‘word,’ thus lessening the prospect of misalignments between the properties, aswell as reducing the complexity of learning.We also enforce regularization policieson the range of notes, thus encouraging the generated melody to stay closeto what humans would find easy to follow.Furthermore, we generate melodyconditioned on song part information, thus replicating the overall structure of afull song.Experimental results demonstrate that our model can generate auditorilypleasant songs that are more indistinguishable from human-written ones thanprevious models.", "target": ["我们提出了一个新的模型来表示音符及其属性，这可以提高自动生成旋律的能力。", "本文提出了西方流行音乐中符号（MIDI）旋律的生成模型，它将音符符号与时间和持续时间信息联合编码，形成音乐 \"词\"。", "本文提出通过将音符表示为 \"词 \"来促进旋律的生成，表示音符的所有属性，从而允许生成音乐 \"句子\"。"]}
+{"source": "Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts.We propose AutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and thus discovers the depth.We propose robust growing and stopping policies to generalize to different network architectures and datasets.Our experiments show that by applying the same policy to different network architectures, AutoGrow can always discover near-optimal depth on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.For example, in terms of accuracy-computation trade-off, AutoGrow discovers a better depth combination in ResNets than human experts.Our AutoGrow is efficient.It discovers depth within similar time of training a single DNN.", "target": ["一种在神经网络中自动增长层数以发现最佳深度的方法。", "一个交错训练较浅的网络和添加新层的框架，它提供了对 \"成长网络 \"范式的见解。"]}
+{"source": "Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.To address it and to speed up innovation in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form.We specifically explore in-domain representation learning and address the question of \"what characteristics should a dataset have to be a good source for remote sensing representation learning\".The established baselines achieve state-of-the-art performance on these datasets.", "target": ["遥感数据集的域内表征学习的探索。", "这篇论文提供了几个标准化的遥感数据集，并表明与在ImageNet上进行微调或从头开始学习相比，域内表示可以为遥感产生更好的基线结果。"]}
+{"source": "Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.They can learn from large unlabeled conversation datasets, build a deep understanding of conversational context, and generate a wide variety of responses.This flexibility comes at the cost of control.Undesirable responses in the training data will be reproduced by the model at inference time, and longer generations often don’t make sense.Instead of generating responses one word at a time, we train a classifier to choose from a predefined list of full responses.The classifier is trained on (conversation context, response class) pairs, where each response class is a noisily labeled group of interchangeable responses.At inference, we generate the exemplar response associated with the predicted response class.Experts can edit and improve these exemplar responses over time without retraining the classifier or invalidating old training data.Human evaluation of 775 unseen doctor/patient conversations shows that this tradeoff improves responses.Only 12% of our discriminative approach’s responses are worse than the doctor’s response in the same conversational context, compared to 18% for the generative model.A discriminative model trained without any manual labeling of response classes achieves equal performance to the generative model.", "target": ["通过使用弱监督来训练分类器以挑选完整的响应，避免一次生成一个词的响应。", "一种使用分类器为医疗对话生成响应的方法，根据对话背景从专家策划的响应中进行选择。"]}
+{"source": "There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs).This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP.In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels.We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical.As a consequence, translation equivariance, beneficial in finite channel CNNs trained with stochastic gradient descent (SGD), is guaranteed to play no role in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case.We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.", "target": ["有限宽度的SGD训练的CNN与无限宽度的完全贝叶斯CNN。谁赢了？", "本文建立了无限通道贝叶斯卷积神经网络和高斯过程之间的联系。"]}
+{"source": "Bayesian inference promises to ground and improve the performance of deep neural networks.It promises to be robust to overfitting, to simplify the training procedure and the space of hyperparameters, and to provide a calibrated measure of uncertainty that can enhance decision making, agent exploration and prediction fairness.Markov Chain Monte Carlo (MCMC) methods enable Bayesian inference by generating samples from the posterior distribution over model parameters.Despite the theoretical advantages of Bayesian inference and the similarity between MCMC and optimization methods, the performance of sampling methods has so far lagged behind  optimization methods for large scale deep learning tasks.We aim to fill this gap and introduce ATMC, an adaptive noise MCMC algorithm that estimates and is able to sample from the posterior of a neural network.ATMC dynamically adjusts the amount of momentum and noise applied to each parameter update in order to compensate for the use of stochastic gradients.We use a ResNet architecture without batch normalization to test ATMC on the Cifar10 benchmark and the large scale ImageNet benchmark and show that, despite the  absence of batch normalization, ATMC outperforms a strong optimization baseline in terms of both classification accuracy and test log-likelihood.We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.", "target": ["我们将贝叶斯推理扩展到ImageNet分类，并取得了具有竞争力的结果准确性和不确定性校准。", "一种用于图像分类的自适应噪声MCMC算法，动态调整应用于每个参数更新的动量和噪声，对过拟合具有稳健性，并提供带有预测的不确定性测量。"]}
+{"source": "Now GANs can generate more and more realistic face images that can easily fool human beings.  In contrast, a common convolutional neural network(CNN), e.g. ResNet-18, can achieve more than 99.9% accuracy in discerning fake/real faces if training and testing faces are from the same source.In this paper, we performed both human studies and CNN experiments, which led us to two important findings.One finding is that the textures of fake faces are substantially different from real ones.CNNs can capture local image texture information for recognizing fake/real face, while such cues are easily overlooked by humans.The other finding is that global image texture information is more robust to image editing and generalizable to fake faces from different GANs and datasets.Based on the above findings, we propose  a  novel  architecture  coined  as  Gram-Net,  which  incorporates  “Gram Block” in multiple semantic levels to extract global image texture representations.Experimental results demonstrate that our Gram-Net performs better than existing approaches for fake face detection.  Especially, our Gram-Net is more robust to image editing, e.g.  downsampling, JPEG compression, blur, and noise.  More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase.", "target": ["一项关于假图像的实证研究显示，纹理是当前假图像与真实图像不同的重要线索。我们改进的捕获全局纹理统计的模型显示出更好的跨GAN假图像检测性能。", "本文提出了一种改进模型性能的方法，用于由GAN生成的图像中的假人脸检测，使其在纹理信息的基础上更具有通用性。"]}
+{"source": "The Wasserstein probability metric has received much attention from the machine learning community.Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes.The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning.In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients.The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third.We provide empirical evidence suggesting this is a serious issue in practice.Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance.We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.We give empirical results on a number of domains comparing these three divergences.To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.", "target": ["Wasserstein距离很难用随机梯度下降法进行最小化，而Cramer距离可以很容易地被优化，而且效果也很好。", "该稿件提出，在使用随机梯度下降法优化目标函数时，使用Cramer距离作为损失，因为它具有无偏的样本梯度。", "文章的贡献与性能标准有关，特别是与瓦尔斯坦/马洛斯的指标有关。"]}
+{"source": "We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.Drawing inspiration from that, we approach the problem of learning an arrow of time in a Markov (Decision) Process.We illustrate how a learned arrow of time can capture salient information about the environment, which in turn can be used to measure reachability, detect side-effects and to obtain an intrinsic reward signal.Finally, we propose a simple yet effective algorithm to parameterize the problem at hand and learn an arrow of time with a function approximator (here, a deep neural network).Our empirical results span a selection of discrete and continuous environments, and demonstrate for a class of stochastic processes that the learned arrow of time agrees reasonably well with a well known notion of an arrow of time due to Jordan, Kinderlehrer and Otto (1998).", "target": ["我们学习MDP的时间之箭，并利用它来衡量可达性，检测副作用，并获得好奇心的奖励信号。", "这项工作提出了h-potential作为衡量MDP中状态转换不对称性目标的解决方案。"]}
+{"source": "We formulate stochastic gradient descent (SGD) as a novel factorised Bayesian filtering problem, in which each parameter is inferred separately, conditioned on the corresopnding backpropagated gradient.  Inference in this setting naturally gives rise to BRMSprop and BAdam: Bayesian variants of RMSprop and Adam.  Remarkably, the Bayesian approach recovers many features of state-of-the-art adaptive SGD methods, including amongst others root-mean-square normalization, Nesterov acceleration and AdamW.  As such, the Bayesian approach provides one explanation for the empirical effectiveness of state-of-the-art adaptive SGD algorithms.  Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST, we find that Bayesian methods have the potential to considerably reduce test loss and classification error.", "target": ["我们将SGD表述为一个贝叶斯滤波问题，并表明这产生了RMSprop、Adam、AdamW、NAG和其他最先进的适应性方法的特征。", "本文通过贝叶斯滤波分析随机梯度下降，作为分析自适应方法的框架。", "作者试图在贝叶斯滤波框架下将现有的适应性梯度方法与动态先验统一起来。"]}
+{"source": "Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks.Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy.Through finding the best policy in well-designed search space of data augmentation, AutoAugment (Cubuk et al., 2019) can significantly improve validation accuracy on image classification tasks.However, this approach is not computationally practical for large-scale problems.In this paper, we develop an adversarial method to arrive at a computationally-affordable solution called Adversarial AutoAugment, which can simultaneously optimize target related object and augmentation policy search loss.The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization.In contrast to prior work, we reuse the computation in target network training for policy evaluation, and dispense with the retraining of the target network.Compared to AutoAugment, this leads to about 12x reduction in computing cost and 11x shortening in time overhead on ImageNet.We show experimental results of our approach on CIFAR-10/CIFAR-100, ImageNet, and demonstrate significant performance improvements over state-of-the-art.On CIFAR-10, we achieve a top-1 test error of 1.36%, which is the currently best performing single model.On ImageNet, we achieve a leading performance of top-1 accuracy 79.40% on ResNet-50 and 80.00% on ResNet-50-D without extra data.", "target": ["我们将对抗性学习的理念引入到自动数据增强中，以提高目标网络的泛化能力。", "一种被称为对抗性自动增强的技术，在训练过程中使用对抗性方法动态地学习良好的数据增强策略。"]}
+{"source": "In this study we focus on first-order meta-learning algorithms that aim to learn a parameter initialization of a network which can quickly adapt to new concepts, given a few examples.We investigate two approaches to enhance generalization and speed of learning of such algorithms, particularly expanding on the Reptile (Nichol et al., 2018) algorithm.We introduce a novel regularization technique called meta-step gradient pruning and also investigate the effects of increasing the depth of network architectures in first-order meta-learning.We present an empirical evaluation of both approaches, where we match benchmark few-shot image classification results with 10 times fewer iterations using Mini-ImageNet dataset and with the use of deeper networks, we attain accuracies that surpass the current benchmarks of few-shot image classification using Omniglot dataset.", "target": ["该研究介绍了两种加强一阶元学习泛化的方法，并提出了对少样本照片图像分类的实证评估。", "本文介绍了对一阶元学习Reptile算法的实证研究，研究了所提出的正则化技术和更深入的网络"]}
+{"source": "In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation.Using in-training matrix factorization, parameter matrices may be decomposed into the products of smaller matrices, which can compress large machine translation architectures by vastly reducing the number of learnable parameters.We apply in-training matrix factorization to different layers of standard neural architectures and show that in-training factorization is capable of reducing nearly 50% of learnable parameters without any associated loss in BLEU score.Further, we find that in-training matrix factorization is especially powerful on embedding layers, providing a simple and effective method to curtail the number of parameters with minimal impact on model performance, and, at times, an increase in performance.", "target": ["本文提出在训练时使用矩阵分解法进行神经机器翻译，这可以在不损害性能的情况下减少模型大小，减少训练时间。", "本文提出在机器翻译的深度神经网络训练过程中使用矩阵分解法压缩模型。"]}
+{"source": "Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge.We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models.We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments.NPIs like 'any' are grammatical only if they appear in a licensing environment like negation ('Sue doesn't have any cats' vs. '*Sue has any cats').This phenomenon is challenging because of the variety of NPI licensing environments that exist.We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments.We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods.We conclude that a variety of methods is necessary to reveal all relevant aspects of a model's grammatical knowledge in a given domain.", "target": ["在一个关于NPI的案例研究中，分析BERT的不同方法提出了不同（但兼容）的结论。"]}
+{"source": "The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes.Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc.A key architectural feature combating such environmental irregularities is ‘long-range horizontal connections’ that aid the perception of the global form of objects.In this work, we explore the introduction of such horizontal connections into standard deep convolutional networks; we present V1Net -- a novel convolutional-recurrent unit that models linear and nonlinear horizontal inhibitory and excitatory connections inspired by primate visual cortical connectivity.We introduce the Texturized Challenge -- a new benchmark to evaluate object recognition performance under perceptual noise -- which we use to evaluate V1Net against an array of carefully selected control models with/without recurrent processing.Additionally, we present results from an ablation study of V1Net demonstrating the utility of diverse neurally inspired horizontal connections for state-of-the-art AI systems on the task of object boundary detection from natural images.We also present the emergence of several biologically plausible horizontal connectivity patterns, namely center-on surround-off, association fields and border-ownership connectivity patterns in a V1Net model trained to perform boundary detection on natural images from the Berkeley Segmentation Dataset 500 (BSDS500).Our findings suggest an increased representational similarity between V1Net and biological visual systems, and highlight the importance of neurally inspired recurrent contextual processing principles for learning visual representations that are robust to perceptual noise and furthering the state-of-the-art in computer vision.", "target": ["在这项工作中，我们提出了V1Net--一个新型的循环神经网络，它模拟了皮质的水平连接，通过知觉分组产生了稳健的视觉表征。", "作者提议修改LSTM的卷积变体，以包括受视觉皮层中已知的相互作用启发的水平连接。"]}
+{"source": "Humans understand novel sentences by composing meanings and roles of core language components.In contrast, neural network models for natural language modeling fail when such compositional generalization is required.The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance.Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models.Throughout a variety of experiments on the SCAN tasks, we analyze the behavior of existing models under the lens of equivariance, and demonstrate that our equivariant architecture is able to achieve the type compositional generalization required in human language understanding.", "target": ["我们提出了排列等价与构成性泛化之间的联系，并提供了等值的语言模型", "这项工作的重点是为SCAN任务的目的，在输入/输出词上学习局部等价表示和函数。"]}
+{"source": "Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks.  A key challenge of variational inference is to approximate the posterior over model parameters with a distribution that is simpler and tractable yet sufficiently expressive.In this work, we propose a method for training highly flexible variational distributions by starting with a coarse approximation and iteratively refining it.Each refinement step makes cheap, local adjustments and only requires optimization of simple variational families.We demonstrate theoretically that our method always improves a bound on the approximation (the Evidence Lower BOund) and observe this empirically across a variety of benchmark tasks.  In experiments, our method consistently outperforms recent variational inference methods for deep learning in terms of log-likelihood and the ELBO.  We see that the gains are further amplified on larger scale models, significantly outperforming standard VI and deep ensembles on residual networks on CIFAR10.", "target": ["本文提出一种算法，通过迭代优化提高贝叶斯神经网络中变分后验的灵活性。", "一种训练柔性的变分后验分布的方法，应用于贝叶斯神经网，对权重进行变分推理（VI）。"]}
+{"source": "In this paper, we propose a residual non-local attention network for high-quality image restoration.Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features.To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block.The trunk branch is used to extract hierarchical features.Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions.The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map.Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network.Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.", "target": ["新的最先进的图像修复框架", "本文提出了一个卷积神经网络架构，其中包括局部和非局部注意力机制的区块，据称这些区块在四个图像修复应用中取得了优异的结果。", "本文提出了一种用于图像修复的残差非局部注意力网络"]}
+{"source": "Most approaches to learning action planning models heavily rely on a significantly large volume of training samples or plan observations.In this paper, we adopt a different approach based on deductive learning from domain-specific knowledge, specifically from logic formulae that specify constraints about the possible states of a given domain.The minimal input observability required by our approach is a single example composed of a full initial state and a partial goal state.We will show that exploiting specific domain knowledge enables to constrain the space of possible action models as well as to complete partial observations, both of which turn out helpful to learn good-quality action models.", "target": ["用专家提供的特定领域的知识来弥补缺乏可用数据的模型获取的混合方法", "一种领域获取方法，考虑通过使用图式化的互斥关系代替前/后条件，对部分领域模型使用不同的表征。"]}
+{"source": "We release the largest public ECG dataset of continuous raw signals for representation learning containing over 11k patients and 2 billion labelled beats.Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events.To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion.  We provide a set of baselines for different feature extractors that can be built upon.  Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify some clustering of known subtypes indicating the potential for representation learning in arrhythmia sub-type discovery.", "target": ["我们发布了一个数据集，该数据集由1.1万名被规定使用{DEVICENAME}(TM)设备的患者的单导联心电图数据构建。", "本文描述了作者打算发表的大规模心电图数据集，并提供了数据集的无监督分析和可视化。"]}
+{"source": "As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is designed to extract local patterns and lacks the ability to model global context in its nature.Many efforts have been recently made to complement CNNs with the global modeling ability, especially by a family of works on global feature interaction.In these works, the global context information is incorporated into local features before they are fed into convolutional layers.However, research on neuroscience reveals that, besides influences changing the inputs to our neurons, the neurons' ability of modifying their functions dynamically according to context is essential for perceptual tasks, which has been overlooked in most of CNNs.Motivated by this, we propose one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context.As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features.Moreover, our proposed CGC is lightweight, amenable to modern CNN architectures, and consistently improves the performance of CNNs according to extensive experiments on image classification, action recognition, and machine translation.", "target": ["一种新型的背景关联卷积，通过明确调节卷积核，将全局背景信息纳入CNN，从而捕捉到更具代表性的局部模式，并提取出判别性的特征。", "本文使用全局背景来调制卷积层的权重，并帮助CNN以高性能和比特征图调制更少的参数捕获更多的鉴别性特征。"]}
+{"source": "We analyze the trade-off between quantization noise and clipping distortion in low precision networks.We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping.By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping.For example, just by choosing the accurate clipping values, more than 40\\% accuracy improvement is obtained for the quantization of VGG-16 to 4-bits of precision.Our results have many applications for the quantization of neural networks at both training and inference time.", "target": ["我们分析了低精度网络中量化噪声和削波失真之间的权衡，并显示了比通常避免削波的标准量化方案的明显改进。", "推导出一个公式，用于寻找均匀量化的最小和最大削波值，使量化产生的平方误差最小，对于预量化值的拉普拉斯分布或高斯分布。"]}
+{"source": "Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field.But its performance can awfully degrade with insufficient batch size.This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of memory consumption.Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.In this paper, we reveal that there are two extra batch statistics involved in backward propagation of BN, on which has never been well discussed before.The extra batch statistics associated with gradients also can severely affect the training of deep neural network.Based on our analysis, we propose a novel normalization method, named Moving Average Batch Normalization (MABN).MABN can completely restore the performance of vanilla BN in small batch cases, without introducing any additional nonlinear operations in inference procedure.We prove the benefits of MABN by both theoretical analysis and experiments.Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO.The code has been released in https://github.com/megvii-model/MABN.", "target": ["我们提出了一种新颖的归一化方法来处理小批量的情况。", "一种处理BN的小批量问题的方法，该方法应用移动平均操作，没有过多的开销，并减少了BN的统计量，以获得更好的稳定性。"]}
+{"source": "We present a simple proof for the benefit of depth in multi-layer feedforward network with rectifed activation (``\"depth separation\").Specifically we present a sequence of classification problems f_i such that(a) for any fixed depth rectified network we can find an index m such that problems with index > m require exponential network width to fully represent the function f_m; and(b) for any problem f_m in the family, we present a concrete neural network with linear depth and bounded width that fully represents it.While there are several previous work showing similar results, our proof uses substantially simpler tools and techniques, and should be accessible to undergraduate students in computer science and people with similar backgrounds.", "target": ["ReLU MLP深度分离证明与gemoteric论证", "证明对于一个系列的问题，较深的网络比较浅的网络需要更多的单元。"]}
+{"source": "The rich and accessible labeled data fuel the revolutionary success of deep learning.Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL).An intuitively feasible approach to FSL is to conduct data augmentation via synthesizing additional training samples.The key to this approach is how to guarantee both discriminability and diversity of the synthesized samples.In this paper, we propose a novel FSL model, called $\\textrm{D}^2$GAN, which synthesizes Diverse and Discriminative features based on Generative Adversarial Networks (GAN).$\\textrm{D}^2$GAN secures discriminability of the synthesized features by constraining them to have high correlation with real features of the same classes while low correlation with those of different classes.  Based on the observation that noise vectors that are closer in the latent code space are more likely to be collapsed into the same mode when mapped to feature space, $\\textrm{D}^2$GAN incorporates a novel anti-collapse regularization term, which encourages feature diversity by penalizing the ratio of the logarithmic similarity of two synthesized features and the logarithmic similarity of the latent codes generating them.Experiments on three common benchmark datasets verify the effectiveness of $\\textrm{D}^2$GAN by comparing with the state-of-the-art.", "target": ["一个新的基于GAN的少样本学习算法，通过合成不同的和有判别力的特征", "一种元学习方法，可以学习一个生成模型，该模型可以增加一个少样本学习者的支持集，该学习者优化了损失的组合。"]}
+{"source": "The lack of crisp mathematical models that capture the structure of real-worlddata sets is a major obstacle to the detailed theoretical understanding of deepneural networks.Here, we first demonstrate the effect of structured data setsby experimentally comparing the dynamics and the performance of two-layernetworks trained on two different data sets:(i) an unstructured synthetic dataset containing random i.i.d. inputs, and(ii) a simple canonical data set suchas MNIST images.Our analysis reveals two phenomena related to the dynamics ofthe networks and their ability to generalise that only appear when training onstructured data sets.Second, we introduce a generative model for data sets,where high-dimensional inputs lie on a lower-dimensional manifold and havelabels that depend only on their position within this manifold.We call it the*hidden manifold model* and we experimentally demonstrate that trainingnetworks on data sets drawn from this model reproduces both the phenomena seenduring training on MNIST.", "target": ["我们证明了数据集的结构是如何影响神经网络的，并为合成数据集引入了一个再现这种影响的生成模型。", "本文研究了数据结构的不同设置如何影响神经网络的学习，以及在合成数据集上学习时如何模仿真实数据集上的行为。"]}
+{"source": "In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones.Besides making a theoretical analysis of their expressivity, we introduced principled techniques for training these models: we devise an initialization scheme and proposed a smart use of non-linearity functions in order to train deep diagonal circulant networks. Furthermore, we show that these networks outperform recently introduced deep networks with other types of structured layers.We conduct a thorough experimental study to compare the performance of deep diagonal circulant networks with state of the art models based on structured matrices and with dense models.We show that our models achieve better accuracy than other structured approaches while required 2x fewer weights as the next best approach.Finally we train deep diagonal circulant networks to build a compact and accurate models on a real world video classification dataset with over 3.8 million training examples.", "target": ["我们训练基于对角线和环形矩阵的深度神经网络，并表明这种类型的网络在现实世界的应用中既紧凑又准确。", "作者对对角环形神经网络（DCNN）的表现力进行了理论分析，并提出了一种深度DCNN的初始化方案。"]}
+{"source": "Interpretability has largely focused on local explanations, i.e. explaining why a model made a particular prediction for a sample.These explanations are appealing due to their simplicity and local fidelity.However, they do not provide information about the general behavior of the model.We propose to leverage model distillation to learn global additive explanations that describe the relationship between input features and model predictions.These global explanations take the form of feature shapes, which are more expressive than feature attributions.Through careful experimentation, we show qualitatively and quantitatively that global additive explanations are able to describe model behavior and yield insights about models such as neural nets.A visualization of our approach applied to a neural net as it is trained is available at https://youtu.be/ErQYwNqzEdc", "target": ["我们建议利用模型蒸馏来学习特征形状形式的全局加性解释（比特征归属更有表现力），用于在表格数据上训练的神经网络等模型。", "本文将广义加权模型（GAMs）与模型蒸馏结合起来，以提供神经网络的全局解释。"]}
+{"source": "A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner.These representations are typically used as general purpose features for words across a range of NLP problems.However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem.Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations.In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences.Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods.We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.", "target": ["具有不同训练目标的大规模多任务学习框架来学习固定长度的句子表征", "本文是关于通过结合几个训练信号来学习句子嵌入：跳过思考、预测翻译、分类蕴含关系和预测成分解析。"]}
+{"source": "In a time where neural networks are increasingly adopted in sensitive applications, algorithmic bias has emerged as an issue with moral implications.While there are myriad ways that a system may be compromised by bias, systematically isolating and evaluating existing systems on such scenarios is non-trivial, i.e., bias may be subtle, natural and inherently difficult to quantify.To this end, this paper proposes the first systematic study of benchmarking state-of-the-art neural models against biased scenarios.More concretely, we postulate that the bias annotator problem can be approximated with neural models, i.e., we propose generative models of latent bias to deliberately and unfairly associate latent features to a specific class.All in all, our framework provides a new way for principled quantification and evaluation of models against biased datasets.Consequently, we find that state-of-the-art NLP models (e.g., BERT, RoBERTa, XLNET) are readily compromised by biased data.", "target": ["我们提出了一个神经性偏差注释器，以衡量模型对有偏见的文本数据集的稳健性。", "一种为NLP生成有偏差的数据集的方法，依赖于条件对抗性正则化自动编码器（CARA）。"]}
+{"source": "We consider the problem of topic modeling in a weakly semi-supervised setting.In this scenario, we assume that the user knows a priori a subset of the topics she wants the model to learn and is able to provide a few exemplar documents for those topics.In addition, while each document may typically consist of multiple topics, we do not assume that the user will identify all its topics exhaustively.      Recent state-of-the-art topic models such as NVDM, referred to herein as Neural Topic Models (NTMs), fall under the variational autoencoder framework.We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.After analyzing the effect of informative priors, we propose a simple modification of the NVDM model using a logit-normal posterior that we show achieves better alignment to user-desired topics versus other NTM models.", "target": ["我们建议通过在每个文档的基础上智能地调整先验来监督VAE式的主题模型。我们发现logit-normal后验提供了最好的性能。", "一种对主题模型进行弱监督的灵活方法，以实现与用户直觉的更好匹配。"]}
+{"source": "Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization ability.However, it is by no means obvious how to estimate mutual information (MI) between each hidden layer and the input/desired output, to construct the IP.For instance, hidden layers with many neurons require MI estimators with robustness towards the high dimensionality associated with such layers.MI estimators should also be able to naturally handle convolutional layers, while at the same time being computationally tractable to scale to large networks.None of the existing IP methods to date have been able to study truly deep Convolutional Neural Networks (CNNs), such as the e.g.\\ VGG-16.In this paper, we propose an IP analysis using the new matrix--based R\\'enyi's entropy coupled with tensor kernels over convolutional layers, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data.The obtained results shed new light on the previous literature concerning small-scale DNNs, however using a completely new approach.Importantly, the new framework enables us to provide the first comprehensive IP analysis of contemporary large-scale DNNs and CNNs, investigating the different training phases and providing new insights into the training dynamics of large-scale neural networks.", "target": ["首次使用基于矩阵的熵和张量核对大规模深度神经网络进行全面的信息平面分析。", "作者提出了一个基于张量核的估计器，用于神经网络中高维层之间的相互信息估计。"]}
+{"source": "Developing agents that can learn to follow natural language instructions has been an emerging research direction.While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans.To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks.We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task.Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy.We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.", "target": ["我们提出了一个模块化的框架，可以完成由程序指定的任务，并实现对更复杂任务的零样本泛化。", "本文研究了用指令和任务分解形式化为程序来训练RL代理，提出了一个程序引导代理的模型，该模型解释了一个程序并向行动模块提出了子目标。"]}
+{"source": "We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function.Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input.We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches.To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions.Our theory also justifies the two-stage learning rate strategy in deep neural networks.While our focus is theoretical, we also present experiments that justify our theoretical findings.", "target": ["我们证明了随机初始化（随机）梯度下降能在多项式时间内学习卷积滤波器。", "研究了使用SGD学习单一卷积滤波器的问题，并表明在���些条件下，SGD能学习单一卷积滤波器。", "本文将高斯分布假设扩展到一个更普遍的角度平滑性假设，它涵盖了更广泛的输入分布系列"]}
+{"source": "Deep neural networks (DNNs) are widely adopted in real-world cognitive applications because of their high accuracy.The robustness of DNN models, however, has been recently challenged by adversarial attacks where small disturbance on input samples may result in misclassification.State-of-the-art defending algorithms, such as adversarial training or robust optimization, improve DNNs' resilience to adversarial attacks by paying high computational costs.Moreover, these approaches are usually designed to defend one or a few known attacking techniques only.The effectiveness to defend other types of attacking methods, especially those that have not yet been discovered or explored, cannot be guaranteed.This work aims for a general approach of enhancing the robustness of DNN models under adversarial attacks.In particular, we propose Bamboo -- the first data augmentation method designed for improving the general robustness of DNN without any hypothesis on the attacking algorithms.Bamboo augments the training data set with a small amount of data uniformly sampled on a fixed radius ball around each training data and hence, effectively increase the distance between natural data points and decision boundary.Our experiments show that Bamboo substantially improve the general robustness against arbitrary types of attacks and noises, achieving better results comparing to previous adversarial training methods, robust optimization methods and other data augmentation methods with the same amount of data points.", "target": ["第一个数据增强方法是专门为提高DNN的一般稳健性而设计的，对攻击算法没有任何假设。", "提出了一种数据增强训练方法，通过从以训练数据为中心的固定半径球体中增强均匀的随机样本，以获得模型的稳健性，对抗对抗性扰动。"]}
+{"source": "The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing.Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons.We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain.We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics.We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks.Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches.Finally, we show how to exploit a trained Spike-GAN  to construct 'importance maps' to detect the most relevant statistical structures present in a spike train. Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience.", "target": ["使用Wasserstein-GAN来产生真实的神经活动，并检测神经群体模式中存在的最相关的特征。", "一种使用半卷积GAN模拟符合经验数据的神经元群的尖峰训练的方法。", "本文提出使用GAN来合成真实的神经活动模式"]}
+{"source": "Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling 2013, Rezende et al. 2014).These approaches maximize a variational lower bound on the intractable log likelihood of the observed data.Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases.Counterintuitively, the typical inference network gradient estimator for the IWAE bound performs poorly as the number of samples increases (Rainforth et al. 2018, Le et al. 2018).Roeder et a.(2017) propose an improved gradient estimator, however, are unable to show it is unbiased.We show that it is in fact biased and that the bias can be estimated efficiently with a second application of the reparameterization trick.The doubly reparameterized gradient (DReG) estimator does not suffer as the number of samples increases, resolving the previously raised issues.The same idea can be used to improve many recently introduced training techniques for latent variable models.In particular, we show that this estimator reduces the variance of the IWAE gradient, the reweighted wake-sleep update (RWS) (Bornschein & Bengio 2014), and the jackknife variational inference (JVI) gradient (Nowozin 2018).Finally, we show that this computationally efficient, drop-in estimator translates to improved performance for all three objectives on several modeling tasks.", "target": ["双重重参数化梯度估计器提供了无偏的方差减少，导致性能的提高。", "作者通过实验发现，现有工作的估计器（STL）是有偏差的，并提出减少偏差以改善ELBO的梯度估计器。"]}
+{"source": "Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable.We analyze our algorithm from a novel geometric perspective and we show that for {\\it any monotone transform} of a smooth and strongly convex objective with latent dimension $k \\ge n$, we present a novel analysis that shows convergence within an $\\epsilon$-ball of the optimum in $O(kQ\\log(n)\\log(R/\\epsilon))$ evaluations, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number.Our rates are the first of its kind to be both1) poly-logarithmically dependent on dimensionality and2) invariant under monotone transformations.We further leverage our geometric perspective to show that our analysis is optimal.Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on synthetic and MuJoCo benchmarks.", "target": ["无梯度下降是一种被证明有效的无梯度算法，对于高维零阶优化来说是单调不变的，而且速度很快。", "本文提出了稳定的无梯度下降（GLD）算法，该算法不依赖于梯度估计。"]}
+{"source": "Many processes can be concisely represented as a sequence of events leading from a starting state to an end state.Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe.Building upon this intuition,  we propose a new class of visual generative models: goal-conditioned predictors (GCP).Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.GCP instead treats videos as start-goal transformations, making video generation easier by conditioning on the more informative context provided by the first and final frames. Not only do existing forward prediction approaches synthesize better and longer videos when modified to become goal-conditioned,  but GCP models can also utilize structures that are not linear in time, to accomplish hierarchical prediction. To this end, we study both auto-regressive GCP models and novel tree-structured GCP models that generate frames recursively, splitting the video iteratively into finer and finer segments delineated by subgoals. In experiments across simulated and real datasets, our GCP methods generate high-quality sequences over long horizons.  Tree-structured GCPs are also substantially easier to parallelize than auto-regressive GCPs, making training  and  inference  very  efficient, and allowing the model to train on sequences that are thousands of frames in length.Finally, we demonstrate the utility of GCP approaches for imitation learning in the setting without access to expert actions. Videos are on the supplementary website: https://sites.google.com/view/video-gcp", "target": ["我们提出了一类新的视觉生成模型：目标条件的预测器。我们通过实验表明，以目标为条件可以减少不确定性，并在更长的时间内产生预测结果。", "本文通过对开始和结束（目标）帧的预测进行调节，将视频预测问题重新表述为内插法而不是外推法，从而获得更高质量的预测。"]}
+{"source": "Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.Most of the medical time series lack annotations or even when the annotations are available they could be subjective and prone to human errors.Earlier works have developed natural language processing techniques to extract concept annotations and/or clinical narratives from doctor notes.However, these approaches are slow and do not use the accompanying medical time series data.To address this issue, we introduce the problem of concept annotation for the medical time series data, i.e., the task of predicting and localizing medical concepts by using the time series data as input.We propose Relational Multi-Instance Learning (RMIL) - a deep Multi Instance Learning framework based on recurrent neural networks, which uses pooling functions and attention mechanisms for the concept annotation tasks.Empirical results on medical datasets show that our proposed models outperform various multi-instance learning models.", "target": ["我们提出了一个基于循环神经网络的深度多实例学习框架，该框架使用池化函数和注意力机制来完成概念注释任务。", "本文涉及医疗时间序列数据的分类，并提出使用循环神经网络架构对每个序列中的实例之间的时间关系进行建模。", "提出了一种新的多实例学习（MIL）公式，称为关系MIL（RMIL），并讨论了它与LSTM、Bi-LSTM、S2S等的一些变体，探讨了RMIL与各种注意力机制的整合，并展示了它在时间序列数据的医学概念预测中的应用。"]}
+{"source": "The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting.We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance.  We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.", "target": ["嵌入层通过张量训练分解进行因子化，以减少其内存占用。", "本文提出了一种低秩张量分解模型，用于对自然语言处理（NLP）中的嵌入矩阵进行参数化，该模型可以压缩网络，有时可以提高测试的准确性。"]}
+{"source": "We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function.We provide empirical evidence that our proposed modification(i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and(ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter).We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence.Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. Our source code will become available after the review process.", "target": ["亚当等自适应梯度方法中的固定权重衰减正则化", "提出了将权重衰减与优化过程中的步骤数脱钩的想法。", "本文提出了一种在Adam中实施权重衰减的替代方法，其实证结果显示", "研究了SGD变体中存在的权重衰减问题，提出了权重衰减和基于梯度的更新之间的解耦方法。"]}
+{"source": "Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning.It is essential towards the development of intelligent machines that can adapt to their surroundings.In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newly observed streaming distributions into our learnt model.We do so through a student-teacher architecture which allows us to learn and preserve all the distributions seen so far without the need to retain the past data nor the past models.Through the introduction of a novel cross-model regularizer, the student model leverages the information learnt by the teacher, which acts as a summary of everything seen till now.The regularizer has the additional benefit of reducing the effect of catastrophic interference that appears when we learn over streaming data.We demonstrate its efficacy on streaming distributions as well as its ability to learn a common latent representation across a complex transfer learning scenario.", "target": ["通过学生-教师结构与跨模型后验正则的结合，实现终身分布式学习。"]}
+{"source": "Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling.In this paper, we look at geometric data represented as point clouds.We introduce a deep autoencoder (AE) network with excellent reconstruction quality and generalization ability.The learned representations outperform the state of the art in 3D recognition tasks and enable basic shape editing applications via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation.We also perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space our AEs and, Gaussian mixture models (GMM).Interestingly, GMMs trained in the latent space of our AEs produce samples of the best fidelity and diversity.To perform our quantitative evaluation of generative models, we propose simple measures of fidelity and diversity based on optimally matching between sets point clouds.", "target": ["深度自动编码器学习几何三维点云数据的良好表示；点云的生成模型。", "使用PointNet架构和latent-space GAN学习GAN类型生成模型的方法。"]}
+{"source": "Despite the remarkable performance of deep neural networks (DNNs) on various tasks, they are susceptible to adversarial perturbations which makes it difficult to deploy them in real-world safety-critical applications.In this paper, we aim to obtain robust networks by sparsifying DNN's latent features sensitive to adversarial perturbation.Specifically, we define vulnerability at the latent feature space and then propose a Bayesian framework to prioritize/prune features based on their contribution to both the original and adversarial loss.We also suggest regularizing the features' vulnerability during training to improve robustness further.While such network sparsification has been primarily studied in the literature for computational efficiency and regularization effect of DNNs, we confirm that it is also useful to design a defense mechanism through quantitative evaluation and qualitative analysis.We validate our method, \\emph{Adversarial Neural Pruning (ANP)} on multiple benchmark datasets, which results in an improvement in test accuracy and leads to state-of-the-art robustness.ANP also tackles the practical problem of obtaining sparse and robust networks at the same time, which could be crucial to ensure adversarial robustness on lightweight networks deployed to computation and memory-limited devices.", "target": ["我们提出了一种新的方法来抑制潜在特征空间的脆弱性，以实现稳健和紧凑的网络。", "本文提出了 \"对抗性神经修剪 \"的方法，即训练一个修剪掩码和一个新的漏洞抑制损失，以提高准确性和对抗性稳健性。"]}
+{"source": "In anomaly detection (AD), one seeks to identify whether a test sample is abnormal,  given a data set of normal samples.   A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs),for unsupervised learning of the normal data distribution.In semi-supervised AD (SSAD), the data also includes a small sample of labeled anomalies.In this work,we propose two variational methods for training VAEs for SSAD.The intuitive idea in both methods is to train the encoder to ‘separate’ between latent vectors for normal and outlier data.We show that this idea can be derived from principled probabilistic formulations of the problem, and propose simple and effective algorithms.  Our methods can be applied to various data types, as we demonstrate on SSAD datasets ranging from natural images to astronomy and medicine, and can be combined with any VAE model architecture.When comparing to state-of-the-art SSAD methods that are not specific to particular data types, we obtain marked improvement in outlier detection.", "target": ["我们提出了两个考虑到负面数据例子的VAE修改，并将其用于半监督性的异常检测。", "这些论文提出了两种类似VAE的半监督新奇性检测方法，即MML-VAE和DP-VAE。"]}
+{"source": "We introduce dynamic instance hardness (DIH) to facilitate the training of machine learning models.DIH is a property of each training sample and is computed as the running mean of the sample's instantaneous hardness as measured over the training history.We use DIH to evaluate how well a model retains knowledge about each training sample over time.We find that for deep neural nets (DNNs), the DIH of a sample in relatively early training stages reflects its DIH in later stages and as a result, DIH can be effectively used to reduce the set of training samples in future epochs.Specifically, during each epoch, only samples with high DIH are trained (since they are historically hard) while samples with low DIH can be safely ignored.DIH is updated each epoch only for the selected samples, so it does not require additional computation.Hence, using DIH during training leads to an appreciable speedup.Also, since the model is focused on the historically more challenging samples, resultant models are more accurate.The above, when formulated as an algorithm, can be seen as a form of curriculum learning, so we call our framework DIH curriculum learning (or DIHCL).The advantages of DIHCL, compared to other curriculum learning approaches, are: (1) DIHCL does not require additional inference steps over the data not selected by DIHCL in each epoch, (2) the dynamic instance hardness, compared to static instance hardness (e.g., instantaneous loss), is more stable as it integrates information over the entire training history up to the present time.Making certain mathematical assumptions, we formulate the problem of DIHCL as finding a curriculum that maximizes a multi-set function $f(\\cdot)$, and derive an approximation bound for a DIH-produced curriculum relative to the optimal curriculum.Empirically, DIHCL-trained DNNs significantly outperform random mini-batch SGD and other recently developed curriculum learning methods in terms of efficiency, early-stage convergence, and final performance, and this is shown in training several state-of-the-art DNNs on 11 modern datasets.", "target": ["对训练动力学的新理解和记忆硬度的衡量标准导致了高效和可证明的课程学习。", "本文将DIH表述为一个课程学习问题，可以更有效地利用数据来训练DNN，并推导出关于近似边界的理论。"]}
+{"source": "This paper explores many immediate connections between adaptive control and machine learning, both through common update laws as well as common concepts.Adaptive control as a field has focused on mathematical rigor and guaranteed convergence.The rapid advances in machine learning on the other hand have brought about a plethora of new techniques and problems for learning.This paper elucidates many of the numerous common connections between both fields such that results from both may be leveraged together to solve new problems.In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.", "target": ["更新规律的平行发展史，以及机器学习中适应性控制和优化之间的概念。"]}
+{"source": "Recurrent convolution (RC) shares the same convolutional kernels and unrolls them multiple times, which is originally proposed to model time-space signals.We suggest that RC can be viewed as a model compression strategy for deep convolutional neural networks.RC reduces the redundancy across layers and is complementary to most existing model compression approaches.However, the performance of an RC network can't match the performance of its corresponding standard one, i.e. with the same depth but independent convolutional kernels.  This reduces the value of RC for model compression.In this paper, we propose a simple variant which improves RC networks: The batch normalization layers of an RC module are learned independently (not shared) for different unrolling steps.We provide insights on why this works.Experiments on CIFAR show that unrolling a convolutional layer several steps can improve the performance, thus indirectly plays a role in model compression.", "target": ["用于模型压缩的循环卷积和一个训练它的技巧，即在步骤上学习独立的BN分层。", "作者用独立的批处理规范化修改了循环卷积神经网络（RCNN），当RCNN包含相同的层数时，RCNN的实验结果与ResNet神经网络架构兼容。"]}
+{"source": "The visual world is vast and varied, but its variations divide into structured and unstructured factors.Structured factors, such as scale and orientation, admit clear theories and efficient representation design.Unstructured factors, such as what it is that makes a cat look like a cat, are too complicated to model analytically, and so require free-form representation learning.We compose structured Gaussian filters and free-form filters, optimized end-to-end, to factorize the representation for efficient yet general learning.Our experiments on dynamic structure, in which the structured filters vary with the input, equal the accuracy of dynamic inference with more degrees of freedom while improving efficiency.(Please see https://arxiv.org/abs/1904.11487 for the full edition.)", "target": ["具有空间高斯结构的动态感受场是准确和有效的。", "本文提出了一个结构化卷积算子来模拟图像局部区域的变形，这大大减少了参数的数量。"]}
+{"source": "It is widely known that well-designed perturbations can cause state-of-the-art machine learning classifiers to mis-label an image, with sufficiently small perturbations that are imperceptible to the human eyes.However, by detecting the inconsistency between the image and wrong label, the human observer would be alerted of the attack.In this paper, we aim to design attacks that not only make classifiers generate wrong labels, but also make the wrong labels imperceptible to human observers.To achieve this, we propose an algorithm called LabelFool which identifies a target label similar to the ground truth label and finds a perturbation of the image for this target label.We first find the target label for an input image by a probability model, then move the input in the feature space towards the target label.Subjective studies on ImageNet show that in the label space, our attack is much less recognizable by human observers, while objective experimental results on ImageNet show that we maintain similar performance in the image space as well as attack rates to state-of-the-art attack algorithms.", "target": ["关于对抗性样本的技巧，使错误分类的标签在标签空间中对人类观察者来说是不可察觉的。", "一种构建对抗性攻击的方法，通过改变目标类别，使其与图像的���始类别相似，从而在图像空间中无成本就能被人类检测到。"]}
+{"source": "This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes.For this study, a collection of floor impact noise dataset is recorded with a single microphone.Noise types/positions are selected based on a report by the Floor Management Center under Korea Environmental Corporation.Using a convolutional neural networks based classifier, the impact noise signals converted to log-scaled Mel-spectrograms are classified into noise types or positions.Also, our model is evaluated on a standard environmental sound dataset ESC-50 to show extensibility on environmental sound classification.", "target": ["本文介绍了建筑物内产生的各种影响噪音的噪音类型/位置分类，这是公寓楼内的一个严重冲突问题。", "这项工作描述了卷积神经网络在建筑噪声类型和噪声位置分类这一新型应用领域中的应用。"]}
+{"source": "Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability.At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics.What determines the dimensionality of activity in neural circuits?What is the functional role of dimensionality in behavior and task learning?In this work we address these questions using recurrent neural network (RNN) models.We find that, depending on the dynamics of the initial network, RNNs learn to increase and reduce dimensionality in a way that matches task demands.These findings shed light on fundamental dynamical mechanisms by which neural networks solve tasks with robust representations that generalize to new cases.", "target": ["循环神经网络学会以与任务相匹配的方式增加和减少其内部表征的维度，这取决于初始网络的动态变化。"]}
+{"source": "Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms.Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, often motivating this approach as minimizing two (of three) terms in a theoretical bound on target error.Unfortunately, this minimization can cause arbitrary increases in the third term, e.g. they can break down under shifting label distributions.We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms.Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets.", "target": ["在传统的深度领域适应性目标中，严格的分布排列在目标标签分布转移时是失败的，而我们提出用新的分析、新的算法和实验验证来优化一个宽松的目标。", "本文提出了领域适应的宽松度量，对目标误差给出了新的理论边界。"]}
+{"source": "In this paper, we explore \\textit{summary-to-article generation}: the task of generating long articles given a short summary, which provides finer-grained content control for the generated text.To prevent sequence-to-sequence (seq2seq) models from degenerating into language models and better controlling the long text to be generated, we propose a hierarchical generation approach which first generates a sketch of intermediate length based on the summary and then completes the article by enriching the generated sketch.To mitigate the discrepancy between the ``oracle'' sketch used during training and the noisy sketch generated during inference, we propose an end-to-end joint training framework based on multi-agent reinforcement learning.For evaluation, we use text summarization corpora by reversing their inputs and outputs, and introduce a novel evaluation method that employs a summarization system to summarize the generated article and test its match with the original input summary.Experiments show that our proposed hierarchical generation approach can generate a coherent and relevant article based on the given summary, yielding significant improvements upon conventional seq2seq models.", "target": ["我们探讨了摘要到文章的生成任务，并提出了一个分层生成方案，以及一个联合的端到端强化学习框架来训练分层模型。", "为了解决摘要到文章生成中的退化问题，本文提出了一种分层生成的方法，首先生成文章的中间草图，然后再生成完整的文章。"]}
+{"source": "When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of(i) “core” or “conditionally invariant” features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and(ii) “style” or “orthogonal” features X^orth whose distribution P(X^orth | Y) can change substantially across domains.These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons.We try to guard against future adversarial domain shifts by ideally just using the “conditionally invariant” features for classification.In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable.We can hence not directly see the distributional change of features across different domains. We do assume, however, that we can sometimes observe a so-called identifier or ID variable.We might know, for example, that two images show the same person, with ID referring to the identity of the person.In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image.The method requires only a small fraction of images to have an ID variable.We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016).However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable.If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian.This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture.We show links to questions of interpretability, fairness and transfer learning.", "target": ["我们提出了反事实正则化，以防止因图像的潜在 \"风格特征 \"的分布变化而产生的对抗性领域转移。", "本文讨论了通过学习一个对图像中的表面变化（或 \"风格 \"特征）不变的分类器，来防范具有反事实正则化的对抗性领域转移的方法。", "本文的目的是针对对抗性领域的转移进行稳健的图像分类，通过避免使用变化的风格特征来实现这一目标。"]}
+{"source": "Gradient-based meta-learning algorithms require several steps of gradient descent to adapt to newly incoming tasks.This process becomes more costly as the number  of  samples  increases.  Moreover,  the  gradient  updates  suffer  from  several sources of noise leading to a degraded performance.   In this work,  we propose a meta-learning algorithm equipped with the GradiEnt Component COrrections, aGECCO cell for short, which generates a multiplicative corrective low-rank matrix which (after vectorization) corrects the estimated gradients. GECCO contains a simple decoder-like network with learnable parameters, an attention module and a so-called context input parameter. The context parameter of GECCO is updated to  generate  a  low-rank  corrective  term  for  the  network  gradients.   As  a  result, meta-learning requires only a few of gradient updates to absorb new task (often, a single update is sufficient in the few-shot scenario). While previous approaches address this problem by altering the learning rates, factorising network parameters or directly learning feature corrections from features and/or gradients, GECCO is an off-the-shelf generator-like unit that performs element-wise gradient corrections without the need to ‘observe’ the features and/or the gradients directly.  We show that our GECCO(i) accelerates learning,(ii) performs robust corrections of the gradients corrupted by a noise, and(iii) leads to notable improvements over existing gradient-based meta-learning algorithms.", "target": ["我们提出了一个元学习器，以便在多个任务上快速适应，甚至是在少样本的设置中一步到位。", "本文提出了一种元学习梯度校正模块的方法，其中预设条件是由神经网络参数化的，并在适应过程中建立了一个两阶段的梯度更新过程。"]}
+{"source": "Discriminative  question  answering  models  can  overfit  to  superficial  biases  in datasets,  because their loss function saturates when any clue makes the answer likely.  We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to answer it.Our  question  answering  (QA)  model  is  implemented  by  learning  a  prior  over answers,  and  a  conditional  language  model  to  generate  the  question  given  the answer—allowing scalable and interpretable many-hop reasoning as the question is generated word-by-word.  Our model achieves competitive performance with specialised discriminative models on the SQUAD and CLEVR benchmarks, indicating that it is a more general architecture for language understanding and reasoning than previous work.The model greatly improves generalisation both from biased training data and to adversarial testing data, achieving a new state-of-the-art on ADVERSARIAL SQUAD.We will release our code.", "target": ["对问题和答案的联合分布进行建模的问答模型可以比判别性模型学到更多东西", "本文提出了一种文本和视觉QA的生成方法，即在给定的背景下学习问题和答案空间的联合分布，这可以捕捉到更复杂的关系。", "本文介绍了一个用于回答问题的生成模型，并建议将p(q,a|c)建模，因子为p(a|c) * p(q|a,c)。 ", "作者提出了一个生成性的QA模型，该模型对问题的分布和给定文件/背景的回答进行联合优化。"]}
+{"source": "In this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is a virtually mandatory technique to train deep networks currently.We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization.Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models.These convolutional neural networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets.The results showed DReLU speeded up learning in all models and datasets.Besides, statistical significant performance assessments (p<0.05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios.Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance.Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function.", "target": ["提出了一个新的激活函数，称为位移整流器线性单元。它被证明可以提高批量归一化卷积神经网络的训练和推理性能。", "本文比较并建议反对使用整流器线性单元后的批量规范化的用法。", "本文提出了一个激活函数，称为位移ReLU，以提高使用批量归一化的CNN的性能。"]}
+{"source": "Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals.We study, in this paper, a scale-equivariant CNN architecture with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve scale-equivariant representations.To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components.A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation.Numerical experiments demonstrate that the proposed scale-equivariant neural network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.", "target": ["我们以最一般的形式构建了标度等价卷积神经网络，既具有计算效率，又被证明具有变形稳健性。", "作者提出了一个CNN架构，该架构通过在激活张量上增加一个额外的标度维，在理论上对各向同性的标度和平移具有等效性。"]}
+{"source": "In this paper, we diagnose deep neural networks for 3D point cloud processing to explore the utility of different network architectures.We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs.In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency.We conduct comparative studies based on such metrics to verify the hypotheses, which may shed new lights on the architectural design of neural networks.Experiments demonstrated the effectiveness of our method.The code will be released when this paper is accepted.", "target": ["我们对三维点云处理的深度神经网络进行了诊断，以探索不同网络架构的效用。", "本文研究了用于3D点云处理的不同神经网络架构，并提出了对抗性稳健性、旋转稳健性和邻域一致性的衡量标准。"]}
+{"source": "In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions.Explicitly defining the structure of the approximation allows to make the variational lower bound tighter, resulting in more accurate inference.", "target": ["利用分布的结构改善半隐式变分推理"]}
+{"source": "Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards.However, it is very difficult to achieve similar success without relying on expert demonstrations.Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior.To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks.We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima.In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states.", "target": ["具有轨迹条件的不同轨迹的自我模仿学习策略", "本文通过对过去经验中多样化的轨迹选择进行自我模仿来解决困难的探索任务，以推动稀疏奖励问题中更有效的探索，实现SOTA结果。"]}
+{"source": "We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost.This is achieved by gating the deep-learning architecture on a fine-grained-level.Individual convolutional maps are turned on/off conditionally on features in the network.To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner.We also introduce a generally applicable tool batch-shaping that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution.We use this novel technique to force gates to be more conditional on the data.We present results on CIFAR-10 and ImageNet datasets for image classification, and Cityscapes for semantic segmentation.Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy.In particular, on ImageNet, our ResNet50 and ResNet34 gated networks obtain 74.60% and 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity.We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples.", "target": ["一种训练大容量神经网络的方法，其准确性显著提高，动态计算成本较低", "使用细粒度的条件选择和新的正则化方法 \"批量整形\"，训练具有大容量的网络，在推理时只使用其中的一部分，取决于输入。"]}
+{"source": "With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity.We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures.The workings of a successfully trained model are visualised to shed some light on how the architecture functions.", "target": ["我们提出了一个端到端的可微分的架构，它可以学习将像素映射到谓词，并在一套简单的关系推理任务上对其进行评估", "基于多头自我注意模块的网络架构，学习一种新的关系表征形式，在课程学习上提高了数据效率和泛化能力。"]}
+{"source": "In natural language inference, the semantics of some words do not affect the inference.Such information is considered superficial and brings overfitting.How can we represent and discard such superficial information?In this paper, we use first order logic (FOL) - a classic technique from meaning representation language – to explain what information is superficial for a given sentence pair.Such explanation also suggests two inductive biases according to its properties.We proposed a neural network-based approach that utilizes the two inductive biases.We obtain substantial improvements over extensive experiments.", "target": ["我们通过从一阶逻辑的角度定义和识别表层信息，利用神经网络将表层信息投射出来，用于自然语言推理。", "本文试图减少自然语言推理中的表面信息，以防止过度拟合，并引入图神经网络来模拟前提和假设之间的关系。", "一种使用一阶逻辑处理自然语言推理的方法，并在NLI模型中注入逻辑信息，使其在推理中更加稳健。"]}
+{"source": "We propose an approach to training machine learning models that are fair in the sense that their performance is invariant under certain perturbations to the features.For example, the performance of a resume screening system should be invariant under changes to the name of the applicant.We formalize this intuitive notion of fairness by connecting it to the original  notion of individual fairness put forth by Dwork et al and show that the proposed approach achieves this notion of fairness.We also demonstrate the effectiveness of the approach on two machine learning tasks that are susceptible to gender and racial biases.", "target": ["利用对抗性稳健性训练个别公平分类器的算法", "本文提出了一个新的算法公平性的定义，以及一个可以证明找到满足公平性约束的ML模型的算法。"]}
+{"source": "In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari.We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also qualitatively by testing a CNN trained on the synthetic data on the real-world datasets.This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.", "target": ["在任何语言中对数字进行分类时，播种和增殖都是你所需要的吗？", "本文介绍了五种语言的新数据集，并提出了一个用于通用数字分类的字体图像数据集生成的新框架（SAT）。"]}
+{"source": "An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning.An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks.Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse,  with the meta initialization already containing high quality features?We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor.This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of the underlying neural network.ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML.We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm).We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.", "target": ["MAML的成功依赖于元初始化的特征重用，这也产生了算法的自然简化，网络主体的内循环被删除，以及对头部和主体的其他见解。", "论文发现，特征重用是MAML成功的主导因素，并提出了新的算法，这些算法花费的计算量比MAML少得多。"]}
+{"source": "Model training remains a dominant financial cost and time investment in machine learning applications.Developing and debugging models often involve iterative training, further exacerbating this issue.With growing interest in increasingly complex models, there is a need for techniques that help to reduce overall training effort.While incremental training can save substantial time and cost by training an existing model on a small subset of data, little work has explored policies for determining when incremental training provides adequate model performance versus full retraining.We provide a method-agnostic algorithm for deciding when to incrementally train versus fully train.We call this setting of non-deterministic full- or incremental training ``Mixed Setting Training\".Upon evaluation in slot-filling tasks, we find that this algorithm provides a bounded error, avoids catastrophic forgetting, and results in a significant speedup over a policy of always fully training.", "target": ["我们提供了一种决定何时进行增量训练与完全训练的方法无关的算法，它比完全训练有明显的速度提升，并避免了灾难性的遗忘。", "本文提出了一种方法，用于决定在填空任务中迭代模型开发的设置下，何时增量与完全重新训练模型。"]}
+{"source": "Neural networks have succeeded in many reasoning tasks.Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many such tasks, while less structured networks fail.Theoretically, there is limited understanding of why and when a network structure generalizes better than other equally expressive ones.We develop a framework to characterize which reasoning tasks a network can learn well, by studying how well its structure aligns with the algorithmic structure of the relevant reasoning procedure.We formally define algorithmic alignment and derive a sample complexity bound that decreases with better alignment.This framework explains the empirical success of popular reasoning models and suggests their limitations.We unify seemingly different reasoning tasks, such as intuitive physics, visual question answering, and shortest paths, via the lens of a powerful algorithmic paradigm, dynamic programming (DP).We show that GNNs can learn DP and thus solve these tasks.On several reasoning tasks, our theory aligns with empirical results.", "target": ["我们开发了一个理论框架来描述神经网络可以很好地学习哪些推理任务。", "本文提出了一种衡量算法一致性的类别，以衡量神经网络与已知算法的 \"接近 \"程度，证明了几类已知算法与神经网络架构之间的联系。"]}
+{"source": "Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses.As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunotherapies.A recent imaging technique for exploring cell-cell interactions, multiplexed ion beam imaging by time-of-flight (MIBI-TOF), allows for cells to be quantified in 36 different protein markers at sub-cellular resolutions in situ as high resolution multiplexed images.To explore the MIBI images, we propose a GAN for multiplexed data with protein specific attention.By conditioning image generation on cell types, sizes, and neighborhoods through semantic segmentation maps, we are able to observe how these factors affect cell-cell interactions simultaneously in different protein channels.Furthermore, we design a set of metrics and offer the first insights towards cell spatial orientations, cell protein expressions, and cell neighborhoods.Our model, cell-cell interaction GAN (CCIGAN), outperforms or matches existing image synthesis methods on all conventional measures and significantly outperforms on biologically motivated metrics.To our knowledge, we are the first to systematically model multiple cellular protein behaviors and interactions under simulated conditions through image synthesis.", "target": ["我们通过使用一个新的注意力GAN架构进行图像合成，探索在高度复用的图像中观察到的跨肿瘤环境背景下的细胞-细胞相互作用。", "通过学习细胞类型和蛋白质标记物表达水平之间的多对多映射，建立了一种新的方法来模拟由多路离子束飞行时间成像（MIBI-TOF）产生的数据。"]}
+{"source": "Machine learning models for question-answering (QA), where given a question and a passage, the learner must select some span in the passage as an answer, are known to be brittle.By inserting a single nuisance sentence into the passage, an adversary can fool the model into selecting the wrong span.A promising new approach for QA decomposes the task into two stages:(i) select relevant sentences from the passage; and(ii) select a span among those sentences.Intuitively, if the sentence selector excludes the offending sentence, then the downstream span selector will be robust.While recent work has hinted at the potential robustness of two-stage QA, these methods have never, to our knowledge, been explicitly combined with adversarial training.This paper offers a thorough empirical investigation of adversarial robustness, demonstrating that although the two-stage approach lags behind single-stage span selection, adversarial training improves its performance significantly, leading to an improvement of over 22 points in F1 score over the adversarially-trained single-stage model.", "target": ["与根据完整上下文训练的单阶段模���相比，由句子选择和跨度选择组成的两阶段方法可以对对抗性攻击更加稳健。", "本文调查了一个现有的模型，发现与其他方法相比，两阶段训练的QA方法对对抗性攻击并不更强大。"]}
+{"source": "The aim of this study is to introduce a formal framework for analysis and synthesis of driver assistance systems.It applies formal methods to the verification of a stochastic human driver model built using the cognitive architecture ACT-R, and then bootstraps safety in semi-autonomous vehicles through the design of provably correct Advanced Driver Assistance Systems.The main contributions include the integration of probabilistic ACT-R models in the formal analysis of semi-autonomous systems and an abstraction technique that enables a finite representation of a large dimensional, continuous system in the form of a Markov model.The effectiveness of the method is illustrated in several case studies under various conditions.", "target": ["基于认知架构的人类驾驶模型的验证，以及根据该模型合成的正确的ADAS。"]}
+{"source": "In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation.However, transliterating between older and newer texts is a laborious task when performed manually.We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model.One approach is implemented, end-to-end, using finite state transducers (FSTs).The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN).We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data.", "target": ["一种新颖的混合深度学习方法为有限的数据问题提供了最佳解决方案（这对保护夏威夷语言非常重要）。"]}
+{"source": "In many real-world settings, a learning model must perform few-shot classification: learn to classify examples from unseen classes using only a few labeled examples per class.Additionally, to be safely deployed, it should have the ability to detect out-of-distribution inputs: examples that do not belong to any of the classes.While both few-shot classification and out-of-distribution detection are popular topics,their combination has not been studied.In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.  Then, we propose two new methods for this task and investigate their performance.In sum, we establish baseline out-of-distribution detection results using standard metrics on new benchmark datasets and show improved results with our proposed methods.", "target": ["我们定量地研究了在少样本下的分布外检测，用ProtoNet、MAML、ABML建立了基线结果，并对其进行了改进。", "本文提出了两个新的置信度分数，它们更适合于少样本分类的分布外检测，并表明基于距离度量的方法提高了性能。"]}
+{"source": "While modern  generative  models are able to synthesize high-fidelity, visually appealing images, successfully generating examples that are useful for recognition tasks remains an elusive goal.To this end, our key insight is that the examples should be synthesized to recover classifier decision boundaries that would be learned from a large amount of real examples.More concretely, we treat a classifier trained on synthetic examples as ''student'' and a classifier trained on real examples as ''teacher''.By introducing knowledge distillation into a meta-learning framework, we encourage the generative model to produce examples in a way that enables the student classifier to mimic the behavior of the teacher.To mitigate the potential gap between student and teacher classifiers, we further propose to distill the knowledge in a progressive manner, either by gradually strengthening the teacher or weakening the student.We demonstrate the use of our model-agnostic distillation approach to deal with data scarcity, significantly improving few-shot learning performance on miniImageNet and ImageNet1K benchmarks.", "target": ["本文介绍了用于学习以识别任务为导向的生成模型的渐进式知识蒸馏法。", "本文展示了由易到难的课程学习，以训练生成模型来改善少样本分类。"]}
+{"source": "Deep neural networks provide state-of-the-art performance for many applications of interest.Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models.Consequently  the adversary can leverage it to attack against the deployed black-box systems. In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability.Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component.Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically.We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance.  These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.", "target": ["我们提出了一种新的方法，通过使用降噪梯度来增强对抗性例子的可转移性。", "本文假设，对抗性扰动由特定模型和特定数据部分组成，而后者的放大最适合于对抗性攻击。", "本文的重点是加强对抗性例子从一个模型到另一个模型的可转移性。"]}
+{"source": "We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).  The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation.Our two-pass CP-decomposition helps prevent from the instability problem.The iterative flow makes the decomposition of the deeper networks systematic.The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.", "target": ["我们提出了迭代的两路CP分解流程，以有效加速现有的卷积神经网络（CNN）。", "本文提出了一种新颖的CNN加速和压缩的工作流程，还提出了一种给定目标整体加速的方法来确定每层的目标等级。", "本文讨论了为深度神经网络（DNN）中的过滤层学习低秩张量过滤操作的问题。"]}
+{"source": "We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bound on the Lipschitz constant of neural networks.The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming.We show how to use the sparse connectivity of a network, to significantly reduce the complexity of computation.This is specially useful for convolutional as well as pruned neural networks.We conduct experiments on networks with random weights as well as networks trained on MNIST, showing that in the particular case of the $\\ell_\\infty$-Lipschitz constant, our approach yields superior estimates as compared to other baselines available in the literature.", "target": ["基于LP的神经网络的Lipschitz常数的上界", "作者研究了估算具有ELO激活函数的深度神经网络的Lipschitz常数问题，将其表述为一个多项式优化问题。"]}
+{"source": "Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of the researches assumed that all meta-training and meta-testing examples came from a single domain.We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including previously unseen ones during meta-training.The key idea is to build a pool of embedding models which have their own metric spaces and to learn to select the best one for a particular task through multi-domain meta-learning.This simplifies task-specific adaptation over a complex task distribution as a simple selection problem rather than modifying the model with a number of parameters at meta-testing time.Inspired by common multi-task learning techniques, we let all models in the pool share a base network and add a separate modulator to each model to refine the base network in its own way.This architecture allows the pool to maintain representational diversity and each model to have domain-invariant representation as well. Experiments show that our selection scheme outperforms other few-shot classification algorithms when target tasks could come from many different domains.They also reveal that aggregating outputs from all constituent models is effective for tasks from unseen domains showing the effectiveness of our framework.", "target": ["我们通过建立多个模型，以集体的方式表示这种复杂的任务分布，并将特定任务的适应性简化为来自这些预训练模型的选择问题，来解决多领域的少样本分类。", "本文通过建立一个嵌入模型池，在不大幅增加参数数量的情况下捕捉领域变量和特定领域的特征，来解决具有许多不同领域的少样本分类问题。"]}
+{"source": "Still in 2019, many scanned documents come into businesses in non-digital format.Text to be extracted from real world documents is often nestled inside rich formatting, such as tabular structures or forms with fill-in-the-blank boxes or underlines whose ink often touches or even strikes through the ink of the text itself.Such ink artifacts can severely interfere with the performance of recognition algorithms or other downstream processing tasks.In this work, we propose DeepErase, a neural preprocessor to erase ink artifacts from text images.We devise a method to programmatically augment text images with real artifacts, and use them to train a segmentation network in an weakly supervised manner.In additional to high segmentation accuracy, we show that our cleansed images achieve a significant boost in downstream recognition accuracy by popular OCR software such as Tesseract 4.0.We test DeepErase on out-of-distribution datasets (NIST SDB) of scanned IRS tax return forms and achieve double-digit improvements in recognition accuracy over baseline for both printed and handwritten text.", "target": ["基于神经的文档墨水人工制品（下划线、污点等）的去除，不使用人工标注的训练数据"]}
+{"source": "Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries.Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost.We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.", "target": ["我们提出了一种查询效率高的黑箱攻击，它使用贝叶斯优化与贝叶斯模型选择相结合，对对抗性扰动和搜索空间降维的最佳程度进行优化。", "作者提出利用贝叶斯优化与GP代理进行对抗性图像生成，通过利用加法结构和使用贝叶斯模型选择来确定最佳降维。"]}
+{"source": "Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information.Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data:1) models must learn the complex intra-modal and cross-modal interactions for prediction and2) models must be robust to unexpected missing or noisy modalities during testing.In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels.We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors.Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction.Modality-specific generative factors are unique for each modality and contain the information required for generating data.Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets.Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance.Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.", "target": ["我们提出了一个学习因子化多模态表征的模型，它具有辨别性、生成性和可解释性。", "本文提出了 \"多模态因子化模型\"，该模型将表征分解为共享的多模态判别因子和特定模态生成因子。 "]}
+{"source": "The successful application of flexible, general learning algorithms to real-world robotics applications is often limited by their poor data-efficiency.To address the challenge, domains with more than one dominant task of interest encourage the sharing of information across tasks to limit required experiment time.To this end, we investigate compositional inductive biases in the form of hierarchical policies as a mechanism for knowledge transfer across tasks in reinforcement learning (RL).We demonstrate that this type of hierarchy enables positive transfer while mitigating negative interference.Furthermore, we demonstrate the benefits of additional incentives to efficiently decompose task solutions.Our experiments show that these incentives are naturally given in multitask learning and can be easily introduced for single objectives.We design an RL algorithm that enables stable and fast learning of structured policies and the effective reuse of both behavior components and transition data across tasks in an off-policy setting.Finally, we evaluate our algorithm in simulated environments as well as physical robot experiments and  demonstrate substantial improvements in data data-efficiency over competitive baselines.", "target": ["我们开发了一种分层的、行为者-批判者的算法，用于通过共享策略组件进行成分转移，并展示了组件的专业化和在多任务领域的相关直接收益，以及对单一任务的适应性。", "不同学习技术的组合，用于获取结构和学习不对称的数据，用于训练HRL政策。", "作者介绍了一种用于单任务和多任务强化学习的分层策略结构，并评估了该结构在复杂机器人任务上的实用性。"]}
+{"source": "In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout.We investigate the complexity of such networks by studying the number of linear regions of the PWL function.Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions.We directly build upon the work of Mont´ufar et al. (2014), Mont´ufar (2017), and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks.In addition to achieving tighter bounds, we also develop a novel method to perform exact numeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output.We use this new capability to visualize how the number of linear regions change while training DNNs.", "target": ["我们根据经验统计了整流器网络的线性区域数量，并完善了上限和下限。", "本文提出了计算ReLU网络中线性区域数量的改进界限。"]}
+{"source": "Convolutional neural networks memorize part of their training data, which is why strategies such as data augmentation and drop-out are employed to mitigate over- fitting.This paper considers the related question of “membership inference”, where the goal is to determine if an image was used during training.We con- sider membership tests over either ensembles of samples or individual samples.First, we show how to detect if a dataset was used to train a model, and in particular whether some validation images were used at train time.Then, we introduce a new approach to infer membership when a few of the top layers are not available or have been fine-tuned, and show that lower layers still carry information about the training samples.To support our findings, we conduct large-scale experiments on Imagenet and subsets of YFCC-100M with modern architectures such as VGG and Resnet.", "target": ["我们通过训练集的convnet来分析记忆特性，并提出了几个我们可以提取训练集的一些信息的用例。", "阐明了大型和深度ConvNets的泛化/记忆特性，并试图开发与识别训练过的ConvNet的输入是否真的被用于训练网络有关的程序。"]}
+{"source": "While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse.The theoretical work of Arora et al. (2017a) suggests a dilemma about GANs’ statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse.By contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators).For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence.This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes.Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.", "target": ["如果判别器类是紧凑的，并且对特定的生成器类有很强的判别能力，那么原则上GAN可以有效地学习分布。", "提出了限制性近似的概念，并提供了一个与维度成多项式的样本复杂度约束，这对于研究GANs的多样性不足是很有用的。", "分析了在一些温和的假设条件下，积分概率度量可以很好地近似于Wasserstein距离。"]}
+{"source": "Understanding the optimization trajectory is critical to understand training of deep neural networks.We show how the hyperparameters of stochastic gradient descent influence the covariance of the gradients (K) and the Hessian of the training loss (H) along this trajectory.Based on a theoretical model, we predict that using a high learning rate or a small batch size in the early phase of training leads SGD to regions of the parameter space with (1) reduced spectral norm of K, and (2) improved conditioning of K and H. We show that the point on the trajectory after which these effects hold, which we refer to as the break-even point, is reached early during training.We demonstrate these effects empirically for a range of deep neural networks applied to multiple different tasks.Finally, we apply our analysis to networks with batch normalization (BN) layers and find that it is necessary to use a high learning rate to achieve loss smoothing effects attributed previously to BN alone.", "target": ["在深度神经网络训练的早期阶段，存在一个 \"收支平衡点\"，它决定了整个优化轨迹的属性。", "这项工作通过考虑批量大小和步长的超参数如何修改学习轨迹，分析了深度神经网络的优化。"]}
+{"source": "Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power.In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors.Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations.Our model, called higher-order weighted GCN (HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.", "target": ["我们提出HWGCN来混合不同顺序的相关邻域信息，以更好地学习节点表征。", "作者提出了一个GCN的变体，即HWGCN，考虑了1步邻居以外的卷积，这与最先进的方法相媲美。"]}
+{"source": "The performance of deep neural networks is often attributed to their automated, task-related feature construction.It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples.Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error.For several flatness measures, this correlation has been empirically validated.However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily.This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization.The proposed measures imply a robustness of the network to changes in the input and the hidden layers.Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data.With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.", "target": ["我们引入了一种新的深度神经网络损失面局部最小值的平坦度测量方法，该方法对于逐层的重新参数化是不变的，我们将平坦度与特征鲁稳健性和泛化相联系。", "作者提出了一个特征稳健性的概念，该概念在重新调整权重方面是不变的，并讨论了该概念与泛化的关系。", "本文定义了一个特征稳健性的概念，并将其与函数的epsilon代表性相结合，描述了深度神经网络中最小值的平坦性和泛化之间的联系。"]}
+{"source": "Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e.g.neurons.We apply and further develop this approach for gated recurrent architectures.Specifically, in addition to sparsification of individual weights and neurons, we propose to sparsify preactivations of gates and information flow in LSTM.It makes some gates and information flow components constant, speeds up forward pass and improves compression.Moreover, the resulting structure of gate sparsity is interpretable and depends on the task.", "target": ["我们建议对LSTM中门的预激活和信息流进行稀疏化处理，使其成为常数，并提高神经元的稀疏程度。", "本文提出了一种循环神经网络的稀疏化方法，通过消除预激活为零的神经元来获得紧凑的网络。"]}
+{"source": "Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems.A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena.This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers.The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data.We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data.We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver.While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.", "target": ["我们引入了一种神经网络方法来辅助偏微分方程求解器。", "作者旨在通过在模拟参考数据上训练一个神经网络来提高数值求解器的精度，该神经网络可以纠正数值求解器。"]}
+{"source": "A patient’s health information is generally fragmented across silos.Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization.Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care.But federated learning cannot handle the situation where different data types for a givenpatient are separated vertically across different organizations.We call methods that enable machine learning model training on data separated by two or more degrees “confederated machine learning.”We built and evaluated a confederated machinelearningmodel to stratify the risk of accidental falls among the elderly.", "target": ["一种联合学习方法，从横向和纵向分离的医疗数据中训练模型", "一种\"联合\"机器学习方法，可以跨学科学习水平和垂直分离的医疗数据。"]}
+{"source": "Existing neural networks are vulnerable to \"adversarial examples\"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks.The most investigated defense strategy is adversarial training which augments training data with adversarial examples.However, applying single-step adversaries in adversarial training does not support the robustness of the networks, instead, they will even make the networks to be overfitted.In contrast to the single-step, multi-step training results in the state-of-the-art performance on MNIST and CIFAR10, yet it needs a massive amount of time.Therefore, we propose a method, Stochastic Quantized Activation (SQA) that solves overfitting problems in single-step adversarial training and fastly achieves the robustness comparable to the multi-step.SQA attenuates the adversarial effects by providing random selectivity to activation functions and allows the network to learn robustness with only single-step training.Throughout the experiment, our method demonstrates the state-of-the-art robustness against one of the strongest white-box attacks as PGD training, but with much less computational cost.Finally, we visualize the learning process of the network with SQA to handle strong adversaries, which is different from existing methods.", "target": ["本文提出了随机量化激活法，解决了FGSM对抗性训练中的过拟合问题，并快速实现了与多步骤训练相媲美的稳健性。", "本文提出了一个模型，通过在其中一个隐藏层的激活中引入随机扰动来改善对抗性训练。"]}
+{"source": "Neural activity is highly variable in response to repeated stimuli.We used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated natural movie presentations.A large fraction of responses are best fit by log-normal distributions or Gaussian mixtures with two components.These distributions are similar to those from units in deep neural networks with dropout.Using a separate set of electrophysiological recordings, we constructed a population coupling model as a control for state-dependent activity fluctuations and found that the model residuals also show non-Gaussian distributions.We then analyzed responses across trials from multiple sections of different movie clips and observed that the noise in cortex aligns better with in-clip versus out-of-clip stimulus variations.We argue that noise is useful for generalization when it moves along representations of different exemplars in-class, similar to the structure of cortical noise.", "target": ["我们研究了大脑中的噪声结构，发现它可能通过沿着类内刺激变化移动表征来帮助泛化。"]}
+{"source": "Unsupervised domain adaptation has received significant attention in recent years.Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories.In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class).The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source.In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain.Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain.These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios.Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain.A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.Furthermore, SE-CC enhances the learnt representation with mutual information maximization.Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.", "target": ["我们提出了一种新的设计，即具有类别无关的集群的自我组装，用于封闭式和开放式领域的适应。", "一种开放集领域适应的新方法，源领域类别包含在目标领域类别中，以便过滤掉离群的类别，实现在共享类别中的适应。"]}
+{"source": "We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics.As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data.We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions.We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets.Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.", "target": ["我们展示了如何用深度学习来学习线性算子的谱系分解，并将其用于没有生成模型的无监督学习。", "作者提出使用深度学习框架来解决最大特征向量的计算问题。", "本文提出了一个通过随机过程学习特征函数的框架，并建议通过使用两阶段的随机优化过程来近似计算大规模背景下的特征函数，以应对这一挑战。"]}
+{"source": "The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs).However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters.This work applies Riemannian stochastic gradient descent (RSGD) to train core tensors of parameters in the Riemannian Manifold before finding vectors of lower Tensor-Train ranks for parameters.The paper first presents the RSGD algorithm with a convergence analysis and then tests it on more advanced Tensor-Train RNNs such as bi-directional GRU/LSTM and Encoder-Decoder RNNs with a Tensor-Train attention model.The experiments on digit recognition and machine translation tasks suggest the effectiveness of the RSGD algorithm for Tensor-Train RNNs.", "target": ["应用黎曼SGD（RSGD）算法来训练张量训练RNN，以进一步减少模型参数。", "本文提出在深度网络中使用黎曼随机梯度算法进行低秩张量训练学习。", "提出了一种基于黎曼优化和等级��应的Tensor Train分解的神经网络优化算法，并设计了一种双向的TT LSTM架构。"]}
+{"source": "In this paper, we consider the problem of learning control policies that optimize areward function while satisfying constraints due to considerations of safety, fairness, or other costs.We propose a new algorithm - Projection Based ConstrainedPolicy Optimization (PCPO), an iterative method for optimizing policies in a two-step process - the first step performs an unconstrained update while the secondstep reconciles the constraint violation by projection the policy back onto the constraint set.We theoretically analyze PCPO and provide a lower bound on rewardimprovement, as well as an upper bound on constraint violation for each policy update.We further characterize the convergence of PCPO with projection basedon two different metrics - L2 norm and Kullback-Leibler divergence.Our empirical results over several control tasks demonstrate that our algorithm achievessuperior performance, averaging more than 3.5 times less constraint violation andaround 15% higher reward compared to state-of-the-art methods.", "target": ["我们提出了一种学习满足约束条件的策略的新算法，并在有约束条件的强化学习背景下提供理论分析和实证证明。", "本文介绍了一种使用两步优化过程的约束性策略优化算法，不满足约束的策略可以被投射回约束集中。"]}
+{"source": "Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data.We attribute this vulnerability to the limitations inherent to activation-based representation.To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information.To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations.We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes.Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets.The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.", "target": ["我们提出了一种基于梯度的表征，用于表征深度网络没有学到的信息。", "作者提出创建基于梯度的权重表征，以补充深度网络训练数据集中缺失的信息。"]}
+{"source": "Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients’ characteristics, surrounding environment, etc.However, existing deep learning based artifact reduction methods are restricted by their training set with specific predetermined artifact type and pattern.As such, they have limited clinical adoption.In this paper, we introduce a “Zero-Shot” medical image Artifact Reduction (ZSAR) framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference.Specifically, we utilize the low internal visual entropy of an image and train a light-weight image-specific artifact reduction network to reduce artifacts in an image at test-time.We use Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) as vehicles to show that ZSAR can reduce artifacts better than state-of-the-art both qualitatively and quantitatively, while using shorter execution time.To the best of our knowledge, this is the first deep learning framework that reduces artifacts in medical images without using a priori training set.", "target": ["我们介绍了一个零样本医学图像伪影减少框架，它利用了深度学习的力量，但没有使用一般的预训练网络或任何干净的图像参考。 "]}
+{"source": "Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks.For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image.In this work we adapt the information bottleneck concept for attribution.By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide.We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings.The method’s information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.", "target": ["我们将信息瓶颈的概念应用于归属问题。", "本文提出了一种新的基于扰动的方法，用于计算基于深度神经网络的图像分类器的归属/显著图，方法是向网络的早期层注入精心制作的噪声。"]}
+{"source": "Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling.Sparsity is a technique to reduce compute and memory requirements of deep learning models.Sparse RNNs are easier to deploy on devices and high-end server processors.Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms.In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization with pruning to create blocks of weights with zeros.Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy.This technique allows us to reduce the model size by roughly 10x.Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count.Our technique works with a variety of block sizes up to 32x32.Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.", "target": ["我们展示了RNN可以被修剪以诱导块状稀疏性，从而提高现有硬件上稀疏操作的速度。", "作者提出了一种压缩RNN的块状稀疏性修剪方法，使用组LASSO来促进稀疏性并进行修剪，但在修剪和修剪权重方面有一个非常专业的安排。"]}
+{"source": "Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable.In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers.The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function.In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).SVIN is designed to produce more effective training gradients through the value iteration network.It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action.We demonstrate the effectiveness of the proposed method in robot motion planning scenarios.In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the  Curiosity rover that is currently operating on Mars.", "target": ["我们提出了对价值迭代网络的改进，并应用于行星探测器的路径规划。", "本文利用价值迭代模块学习基于专家轨迹的奖励函数，以使规划步骤具有可分性"]}
+{"source": "Transformer networks have lead to important progress in language modeling and machine translation.These models include two consecutive modules, a feed-forward layer and a self-attention layer.The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers.Building upon this intuition, we propose a new model that solely consists of attention layers.More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer.Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer.Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.", "target": ["一种新型的注意层，结合了Transformer网络的自我注意和前馈子层。", "本文提出了对Transformer模型的修改，将对 \"持久性 \"记忆向量的注意纳入自我注意层，使其性能与现有模型相当，同时使用较少的参数。"]}
+{"source": "This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing a group of anomalous inputs.  Detecting anomalies is a critical component for multiple machine learning problems including detecting the presence of adversarial noise added to inputs.More broadly, this work is a step towards giving neural networks the ability to detect groups of out-of-distribution samples.  This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.  Subset Scanning allows us to answer the question: \"``Which subset of inputs have larger-than-expected activations at which subset of nodes?\"  Framing the adversarial detection problem this way allows us to identify systematic patterns in the activation space that span multiple adversarially noised images.  Such images are ``\"weird together\".  Leveraging this common anomalous pattern, we show increased detection power as the proportion of noised images increases in a test set.   Detection power and accuracy results are provided for targeted adversarial noise added to CIFAR-10 images on a 20-layer ResNet using the Basic Iterative Method attack.", "target": ["我们有效地找到一个图像子集，该子集的一些节点的激活度高于预期。  这些图像在作为一个群体来看时，显得更加异常，更容易被发现。", "该论文提出了一种基于 \"子集扫描 \"的方案来检测异常输入的存在，以检测深度学习网络中的异常激活。"]}
+{"source": "Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks.In this work, we conduct a thorough investigation of stable recurrent models.Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent.Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks.Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime.Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.", "target": ["稳定的循环模型可以用前馈网络来近似，根据经验，在基准任务上的表现与不稳定的模型一样好。", "研究RNN的稳定性，并调查频谱归一化对序列预测的影响。"]}
+{"source": "Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network.But understanding how weight-sharing can be used effectively in general is a topic that has not been studied extensively.Chen et al. (2015) proposed HashedNets, which augments a multi-layer perceptron with a hash table, as a method for neural network compression.We generalize this method into a framework (ArbNets) that allows for efficient arbitrary weight-sharing, and use it to study the role of weight-sharing in neural networks.We show that common neural networks can be expressed as ArbNets with different hash functions.We also present two novel hash functions, the Dirichlet hash and the Neighborhood hash, and use them to demonstrate experimentally that balanced and deterministic weight-sharing helps with the performance of a neural network.", "target": ["研究了使用哈希函数的神经网络中权重共享的作用，发现平衡和确定的哈希函数有助于网络性能。", "提出ArbNets，通过将分权函数定义为哈希函数，以更系统的方式研究分权。"]}
+{"source": "We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules.Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure.Interestingly, any MLN can be represented as an NMLN.Similarly to recently proposed Neural theorem provers (NTPs) (Rocktaschel at al. 2017), NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence.This is extremely important for predicting in settings other than the transductive one.We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.", "target": ["我们介绍了一个统计关系学习系统，它借用了马尔可夫逻辑的思想，但学习的是作为神经网络的规则隐含表征。", "本文对马尔科夫逻辑网络进行了扩展，取消了其对预定的一阶逻辑规则的依赖，以在知识库完成任务中对更多的领域进行建模。"]}
+{"source": "Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks.This results in a rich prior capable of few-shot learning on new tasks.The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments.Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them.Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning.Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.", "target": ["一种在多个任务中学习神经网络上的表达性先验的可扩展方法。", "本文提出了一种训练多任务迁移学习的概率模型的方法，通过为每个任务引入一个潜在变量来捕捉任务实例的共性。", "该工作提出了一种元学习的变分方法，采用了与特定任务数据集相对应的潜变量。", "旨在为多个任务学习神经网络的先验。"]}
+{"source": "Sequential data often originates from diverse environments.Across them exist both shared regularities and environment specifics.To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM).In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics.We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization.We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters.In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.", "target": ["分散的状态空间模型", "本文提出了一个生成的状态空间模型，使用一个全局潜变量E来捕捉环境的特定信息。"]}
+{"source": "In this work, we approach one-shot and few-shot learning problems as methods for finding good prototypes for each class, where these prototypes are generalizable to new data samples and classes.We propose a metric learner that learns a Bregman divergence by learning its underlying convex function.Bregman divergences are a good candidate for this framework given they are the only class of divergences with the property that the best representative of a set of points is given by its mean.We propose a flexible extension to prototypical networks to enable joint learning of the embedding and the divergence, while preserving computational efficiency.Our preliminary results are comparable with the prior work on the Omniglot and Mini-ImageNet datasets, two standard benchmarks for one-shot and few-shot learning.We argue that our model can be used for other tasks that involve metric learning or tasks that require approximate convexity such as structured prediction and data completion.", "target": ["布雷格曼发散学习，用于少样本学习。"]}
+{"source": "Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime,we introduce the Unrestricted Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent.We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of differ-ent depths and widths) can emerge dynamically from the same URN.These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network.We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.", "target": ["我们引入了一个网络框架，它可以在训练过程中修改其结构，并表明它可以收敛到各种ML网络原型，如MLP和LCN。"]}
+{"source": "We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains.CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain.Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training.In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains.We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances.CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives.This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions.Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and (2) data augmentation is a more stable and accurate method than domain adversarial training.", "target": ["领域指导下的���据增强提供了一种稳健而稳定的领域泛化方法", "本文提出了一种通过依赖领域的数据增强的领域泛化方法", "作者介绍了CrossGrad方法，该方法同时训练一个标签分类任务和一个领域分类任务。"]}
+{"source": "We present sketch-rnn, a recurrent neural network able to construct stroke-based drawings of common objects.The model is trained on a dataset of human-drawn images representing many different classes.We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.", "target": ["我们研究了替代传统像素图像建模的方法，并提出了一个矢量图像的生成模型。", "本文介绍了一种用于生成草图的神经网络架构，其灵感来自于变分自动编码器。"]}
+{"source": "Wilson et al. (2017) showed that, when the stepsize schedule is properly designed, stochastic gradient generalizes better than ADAM (Kingma & Ba, 2014).In light of recent work on hypergradient methods (Baydin et al., 2018), we revisit these claims to see if such methods close the gap between the most popular optimizers.As a byproduct, we analyze the true benefit of these hypergradient methods compared to more classical schedules, such as the fixed decay of Wilson et al. (2017).In particular, we observe they are of marginal help since their performance varies significantly when tuning their hyperparameters.Finally, as robustness is a critical quality of an optimizer, we provide a sensitivity analysis of these gradient based optimizers to assess how challenging their tuning is.", "target": ["我们提供了一项研究，试图看看最近的在线学习率适应如何扩展Wilson等人2018年关于自适应梯度方法的结论，同时还进行了比较和敏感性分析。", "报告了几个与步骤调整有关的方法的测试结果，包括vanilla SGD、带有Neserov动量的SGD和ADAM，并比较了这些方法与超梯度和没有超梯度的情况。"]}
+{"source": "Despite an ever growing literature on reinforcement learning algorithms and applications, much less is known about their statistical inference.In this paper, we investigate the large-sample behaviors of the Q-value estimates with closed-form characterizations of the asymptotic variances.This allows us to efficiently construct confidence regions for Q-value and optimal value functions, and to develop policies to minimize their estimation errors.This also leads to a policy exploration strategy that relies on estimating the relative discrepancies among the Q estimates.Numerical experiments show superior performances of our exploration strategy than other benchmark approaches.", "target": ["我们研究了Q值估计的大样本行为，并提出了一个有效的探索策略，该策略依赖于对Q值估计之间的相对差异的估计。", "本文提出了一种基于Q值的渐进分析及其向中心极限分布收敛的强化学习的纯探索算法，其表现优于基准探索算法。"]}
+{"source": "We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e.g., class, size, shape, etc). In particular, we are interested in a more difficult case than those typically addressed in the literature, where the source and target are ``far\" enough that reconstruction-style or pixel-wise approaches fail.We argue that transferring (i.e., \\emph{translating}) said relevant information should involve both discarding source domain-specific information while incorporate target domain-specific information, the latter of which we model with a noisy prior distribution. In order to avoid the degenerate case where the generated samples are only explained by the prior distribution, we propose to minimize an estimate of the mutual information between the generated sample and the sample from the prior distribution.We discover that the architectural choices are an important factor to consider in order to preserve the shared semantic between $X$ and $Y$. We show state of the art results on the MNIST to SVHN task for unsupervised image to image translation.", "target": ["我们训练一个图像转换网络，将源图像和先验分布的样本作为输入，生成目标分布的样本。", "本文对无监督转换的问题进行了形式化，并提出了一个增强的GAN框架，该框架使用互相的信息来避免退化的情况。", "本文提出了无监督的一对多图像转换问题，并通过最小化相互信息量来解决这个问题。"]}
+{"source": "Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms.Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks.  However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators.We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description.We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution.Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.", "target": ["学习从代理任务中提取可区分的关键点，剔除离群点。", "本文致力于使用神经引导的RANSAC作为额外的辅助损失提供者来改进描述符插值，对局部特征进行自我监督学习。"]}
+{"source": "We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually.Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent.We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy.While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken.We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both1) training with only the sparse reward and2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior.Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.", "target": ["我们提出了一种内在动机的表述，它适合作为多代理稀疏奖励协同任务中的探索偏向，通过鼓励代理以单独行动时无法实现的方式影响世界。", "本文重点讨论了在需要多代理实现的任务中，利用内在动机来改善强化学习代理的探索过程。"]}
+{"source": "A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process.In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole information aggregation as stochastic sequential processes.The algorithm works on a much larger search space, utilizes reasoning history to perform inference, and is robust to noisy edges.We apply our algorithm to multiple complex graph reasoning and prediction tasks and show that our algorithm consistently outperforms state-of-the-art graph-structured models by a significant margin.", "target": ["由神经网络驱动的图结构模型的概率推理算法", "本文介绍了策略性消息传递，这是一个具有推理机制的图神经网络，它以循环的方式将消息分配给边，表明在视觉推理任务上具有竞争性的性能。"]}
+{"source": "Deep multitask networks, in which one neural network produces multiple predictive outputs, are more scalable and often better regularized than their single-task counterparts.Such advantages can potentially lead to gains in both speed and performance, but multitask networks are also difficult to train without finding the right balance between tasks.We present a novel gradient normalization (GradNorm) technique which automatically balances the multitask loss function by directly tuning the gradients to equalize task training rates.We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting over single networks, static baselines, and other adaptive multitask loss balancing techniques.GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter $\\alpha$.Thus, what was once a tedious search process which incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks.Ultimately, we hope to demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.", "target": ["我们展示了如何通过调整一个自适应的多任务损失函数来提高多任务网络的性能，该函数是通过直接平衡网络梯度学习的。", "这项工作提出了一个动态权重更新方案，通过利用不同任务的损失率，在训练时间内更新不同任务损失的权重。"]}
+{"source": "Image segmentation aims at grouping pixels that belong to the same object or region.At the heart of image segmentation lies the problem of determining whether a pixel is inside or outside a region, which we denote as the \"insideness\" problem.Many Deep Neural Networks (DNNs) variants excel in segmentation benchmarks, but regarding insideness, they have not been well visualized or understood: What representations do DNNs use to address the long-range relationships of insideness?How do architectural choices affect the learning of these representations?In this paper, we take the reductionist approach by analyzing DNNs solving the insideness problem in isolation, i.e. determining the inside of closed (Jordan) curves.We demonstrate analytically that state-of-the-art feed-forward and recurrent architectures can implement solutions of the insideness problem for any given curve.Yet, only recurrent networks could  learn these general solutions when the training enforced a specific \"routine\" capable of breaking down the long-range relationships.Our results highlights the need for new training strategies that decompose the learning into appropriate stages, and that lead to the general class of solutions necessary for DNNs to understand insideness.", "target": ["用于图像分割的DNN可以实现内部性问题的解决方案，但只有一些循环网络可以在特定类型的监督下学习它们。", "本文引入insideness来研究深度学习时代的语义分割，结果可以帮助模型更好地泛化。"]}
+{"source": "We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks.Specifically, we propose to explore gradient-based features.These features are gradients of the model parameters with respect to a task-specific loss given an input sample.Our key innovation is the design of a linear model that incorporates both gradient features and the activation of the network.We show that our model provides a local linear approximation to a underlying deep model, and discuss important theoretical insight.Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradients.Our method is evaluated across a number of representation learning tasks on several datasets and using different network architectures.We demonstrate strong results in all settings.And our results are well-aligned with our theoretical insight.", "target": ["考虑到一个预先训练好的模型，我们探索了模型参数相对于特定任务损失的每个样本梯度，并构建了一个结合模型参数梯度和模型激活的线性模型。", "本文提出将卷积网络特定层的梯度作为线性化模型中的特征，用于转移学习和快速适应。"]}
+{"source": "Recovering 3D geometry shape, albedo and lighting from a single image has wide applications in many areas, which is also a typical ill-posed problem.In order to eliminate the ambiguity, face prior knowledge like linear 3D morphable models (3DMM) learned from limited scan data are often adopted to the reconstruction process.However, methods based on linear parametric models cannot generalize well for facial images in the wild with various ages, ethnicity, expressions, poses, and lightings.Recent methods aim to learn a nonlinear parametric model using convolutional neural networks (CNN) to regress the face shape and texture directly.However, the models were only trained on a dataset that is generated from a linear 3DMM.Moreover, the identity and expression representations are entangled in these models, which hurdles many facial editing applications.In this paper, we train our model with adversarial loss in a semi-supervised manner on hybrid batches of unlabeled and labeled face images to exploit the value of large amounts of unlabeled face images from unconstrained photo collections.A novel center loss is introduced to make sure that different facial images from the same person have the same identity shape and albedo.Besides, our proposed model disentangles identity, expression, pose, and lighting representations, which improves the overall reconstruction performance and facilitates facial editing applications, e.g., expression transfer.Comprehensive experiments demonstrate that our model produces high-quality reconstruction compared to state-of-the-art methods and is robust to various expression, pose, and lighting conditions.", "target": ["我们以半监督的方式在未标记和标���的混合批次的人脸图像上训练我们的人脸重建模型，以利用来自无约束的照片集的大量未标记的人脸图像的价值。", "本文提出了一个半监督和对抗性的训练过程，从具有损失函数的人脸图像中精确地获得非线性的分离表征，在人脸重建中达到最先进的性能。"]}
+{"source": "Human conversations naturally evolve around related entities and connected concepts, while may also shift from topic to topic.This paper presents ConceptFlow, which leverages commonsense knowledge graphs to explicitly model such conversation flows for better conversation response generation.ConceptFlow grounds the conversation inputs to the latent concept space and represents the potential conversation flow as a concept flow along the commonsense relations.The concept is guided by a graph attention mechanism that models the possibility of the conversation evolving towards different concepts.The conversation response is then decoded using the encodings of both utterance texts and concept flows, integrating the learned conversation structure in the concept space.Our experiments on Reddit conversations demonstrate the advantage of ConceptFlow over previous commonsense aware dialog models and fine-tuned GPT-2 models, while using much fewer parameters but with explicit modeling of conversation structures.", "target": ["本文提出了ConceptFlow，它在常识性知识图中明确地对对话流进行建模，以更好地生成对话。", "本文提出了一个系统，用于在开放领域的对话环境中，利用扩散到基础概念的邻居中，生成对所发布的语词的单匝回应。"]}
+{"source": "Biological neural networks face homeostatic and resource constraints that restrict the allowed configurations of connection weights.If a constraint is tight it defines a very small solution space, and the size of these constraint spaces determines their potential overlap with the solutions for computational tasks.We study the geometry of the solution spaces for constraints on neurons' total synaptic weight and on individual synaptic weights, characterizing the connection degrees (numbers of partners) that maximize the size of these solution spaces.We then hypothesize that the size of constraints' solution spaces could serve as a cost function governing neural circuit development.We develop analytical approximations and bounds for the model evidence of the maximum entropy degree distributions under these cost functions.We test these on a published electron microscopic connectome of an associative learning center in the fly brain, finding evidence for a developmental progression in circuit structure.", "target": ["我们研究了这样一个假设：对突触权重进行约束的解空间的熵（约束的 \"灵活性\"）可以作为神经回路发展的成本函数。"]}
+{"source": "In  this  preliminary  work,  we  study  the  generalization  properties  of  infinite  ensembles  of infinitely-wide neural networks.  Amazingly, this model family admits tractable calculations for many information-theoretic quantities.  We report analytical and empirical investigations in the search for signals that correlate with generalization.", "target": ["从信息论的角度来看，无限宽的神经网络的无限集合是一个有趣的模型系列。"]}
+{"source": "Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly.In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse cross-lingual tasks.Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is task-dependent.Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches.Extensive experiments on various tasks demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark.We further show that our proposed framework can generalize to contextualized representations and achieves state-of-the-art results on the CoNLL cross-lingual NER benchmark.", "target": ["我们对跨语言一致与联合训练方法进行了比较研究，并在一个新的框架中统一了这两个以前排他性的范式。", "本文比较了双语词库归纳的方法，并说明哪种方法在词库、归纳、NER和MT任务上表现更好。"]}
+{"source": "Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as \"inferencing as a service\" environments on the cloud. Prior work has considered reduction in the size of the models, through compression techniques like weight pruning, filter pruning, etc. or through low-rank decomposition of the convolution layers.In this paper, we demonstrate the use of multiple techniques to achieve not only higher model compression but also reduce the compute resources required during inferencing.We do filter pruning followed by low-rank decomposition using Tucker decomposition for model compression.We show that our approach achieves upto 57\\% higher model compression when compared to either Tucker Decomposition or Filter pruning alone  at similar accuracy for GoogleNet.Also, it reduces the Flops by upto 48\\% thereby making the inferencing faster.", "target": ["结合正交的模型压缩技术，在推理过程中获得模型大小和所需的翻转次数的显著减少。", "本文提出将Tucker分解与滤波器修剪相结合。"]}
+{"source": "We review the limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, and introduce JAUNE:  a set of criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.", "target": ["介绍JAUNE：一种用多维的、基于模型的评价器来取代BLEU和ROUGE分数的方法，用于评估总结。", "本文提出了一个新的JAUNE指标，用于评估机器翻译和文本总结系统，表明他们的模型比BLEU更好地对应于基本事实相似度标签。"]}
+{"source": "This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM).Many standard GNN variants propagate information along the edges of a graph by computing ``messages'' based only on the representation of the source of each edge.In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information.Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods.Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature.Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.", "target": ["新的GNN形式主义+广泛的实验；显示GGNN/GCN/GAT之间的差异比想象的要小", "本文提出了一种新的图神经网络架构，该架构使用特征线性调制（Feature-wise Linear Modulation），根据目标节点的表示，对源节点到目标节点的信息传递进行调节。"]}
+{"source": "To deal simultaneously with both, the attributed network embedding and clustering, we propose a new model.It exploits both content and structure information, capitalising on their simultaneous use.The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering one.Thereby, we show that incorporating an embedding representation provides simpler and more interpretable solutions.Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering and embedding, than the state-of-art algorithms, including deep learning methods devoted to similar tasks for attributed network datasets with different proprieties.", "target": ["本文提出了一个新颖的矩阵分解框架，用于同时进行属性网络数据的嵌入和聚类。", "本文提出了一种共同进行属性网络嵌入和聚类的算法。"]}
+{"source": "We propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis.The goal of our method is to generate photo-realistic re-renderings of reconstructed objects for virtual and augmented reality applications (e.g., virtual showrooms, virtual tours and sightseeing, the digital inspection of historical artifacts).A core component of our work is the handling of view-dependent effects.Specifically, we directly train an object-specific deep neural network to synthesize the view-dependent appearance of an object.As input data we are using an RGB video of the object.This video is used to reconstruct a proxy geometry of the object via multi-view stereo.Based on this 3D proxy, the appearance of a captured view can be warped into a new target view as in classical image-based rendering.This warping assumes diffuse surfaces, in case of view-dependent effects, such as specular highlights, it leads to artifacts.To this end, we propose EffectsNet, a deep neural network that predicts view-dependent effects.Based on these estimations, we are able to convert observed images to diffuse images.These diffuse images can be projected into other views.In the target view, our pipeline reinserts the new view-dependent effects.To composite multiple reprojected images to a final output, we learn a composition network that outputs photo-realistic results.Using this image-guided approach, the network does not have to allocate capacity on ``remembering'' object appearance, instead it learns how to combine the appearance of captured images.We demonstrate the effectiveness of our approach both qualitatively and quantitatively on synthetic as well as on real data.", "target": ["我们提出了一种学习型图像引导的渲染技术，该技术结合了基于图像的渲染和基于GAN的图像合成的优点，同时考虑了与视图相关的效果。", "本报告提出了一种处理神经渲染中视线依赖效应的方法，它提高了现有神经渲染方法的稳健性。"]}
+{"source": "We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets.The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes.We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support or sharp bends with noise.Additionally, on image datasets, we find that GANs do not seem to learn to count the number of objects of the same kind in an image.We also highlight the apparent tension between generalization and learning in GANs.", "target": ["在合成数据集上对GAN进行评估"]}
+{"source": "This paper proposes a new approach for step size adaptation in gradient methods.The proposed method called step size optimization (SSO) formulates the step size adaptation as an optimization problem which minimizes the loss function with respect to the step size for the given model parameters and gradients.Then, the step size is optimized based on alternating direction method of multipliers (ADMM).SSO does not require the second-order information or any probabilistic models for adapting the step size, so it is efficient and easy to implement.Furthermore, we also introduce stochastic SSO for stochastic learning environments.In the experiments, we integrated SSO to vanilla SGD and Adam, and they outperformed state-of-the-art adaptive gradient methods including RMSProp, Adam, L4-Adam, and AdaBound on extensive benchmark datasets.", "target": ["我们为梯度方法提出了一种高效的、有效的步长适应方法。", "一阶梯度方法中新的步长适应性，用损失函数的一阶扩展和正则化建立了一个新的优化问题，其中步长被视为变量。"]}
+{"source": "Despite the fact that generative models are extremely successful in practice, the theory underlying this phenomenon is only starting to catch up with practice.In this work we address the question of the universality of generative models: is it true that neural networks can approximate any data manifold arbitrarily well?We provide a positive answer to this question and show that under mild assumptions on the activation function one can always find a feedforward neural network that maps the latent space onto a set located within the specified Hausdorff distance from the desired data manifold.We also prove similar theorems for the case of multiclass generative models and cycle generative models, trained to map samples from one manifold to another and vice versa.", "target": ["我们拍到，ReLU和sigmoid网络可以以任意的精度生成一类广泛的流形。", "本文对流形何时能被写成由神经网络近似的地图的图像提供了某些基本保证，并将流形几何的定理和标准的普遍近似结果缝合在一起。", "本文从理论上表明，基于神经网络的生成模型可以近似于数据流形，并证明在温和的假设下，神经网络可以在很小的Hausdorff距离内将潜在空间映射到接近于给定数据流形的集合。"]}
+{"source": "Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.However, the theoretical understanding of such methods has been rather limited.This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward.The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model.The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires no explicit uncertainty quantification.Instantiating our framework with simplification gives a  variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO).Experiments demonstrate that SLBO achieves the state-of-the-art performance when only 1M or fewer samples are permitted on a range of continuous control benchmark tasks.", "target": ["我们设计了具有理论保证的基于模型的强化学习算��，并在允许一百万或更少样本的情况下，在Mujuco基准任务上取得最先进的结果。", "本文提出了一个框架，设计基于OFU的RL算法，在MuJoCo任务上实现了SOTA性能。"]}
+{"source": "We study the use of knowledge distillation to compress the U-net architecture.We show that, while standard distillation is not sufficient to reliably train a compressed U-net, introducing other regularization methods, such as batch normalization and class re-weighting, in knowledge distillation significantly improves the training process.This allows us to compress a U-net by over 1000x, i.e., to 0.1% of its original number of parameters, at a negligible decrease in performance.", "target": ["我们提出了额外的技术，利用知识蒸馏将U-net压缩1000倍以上。", "作者介绍了一种改进的蒸馏策略，将U-net结构压缩了1000多倍，同时保留了接近原始U-net的精度。"]}
+{"source": "Learning neural networks with gradient descent over a long sequence of tasks is problematic as their fine-tuning to new tasks overwrites the network weights that are important for previous tasks.This leads to a poor performance on old tasks – a phenomenon framed as catastrophic forgetting.  While early approaches use task rehearsal and growing networks that both limit the scalability of the task sequence orthogonal approaches build on regularization.  Based on the Fisher information matrix (FIM) changes to parameters that are relevant to old tasks are penalized, which forces the task to be mapped into the available remaining capacity of the network.This requires to calculate the Hessian around a mode, which makes learning tractable.In this paper, we introduce Hessian-free curvature estimates as an alternative method to actually calculating the Hessian.  In contrast to previous work, we exploit the fact that most regions in the loss surface are flat and hence only calculate a Hessian-vector-product around the surface that is relevant for the current task.Our experiments show that on a variety of well-known task sequences we either significantly outperform or are en par with previous work.", "target": ["本文提供了一种通过无Hessian的曲率估计来解决灾难性遗忘的方法", "本文提出了一种近似的拉普拉斯方法在持续学习环境下的神经网络训练中具有较低的空间复杂性。"]}
+{"source": "There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constrained settings as well as environmental considerations.In this paper, we propose a novel method SRatio that can utilize information from high performing complex models (viz. deep neural networks, boosted trees, random forests) to reweight a training dataset for a potentially low performing simple model such as a decision tree or a shallow network enhancing its performance.Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel.Moreover, we generalize and formalize the concept of attaching probes to intermediate layers of a neural network, which was one of the main ideas in previous work \\citep{profweight}, to other commonly used classifiers and incorporate this into our method.The benefit of these contributions is witnessed in the experiments where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority (16 out of 27) of the cases and tie for best performance in the remaining cases.In fact, in a couple of cases, we even approach the complex model's performance.We also conduct further experiments to validate assertions and intuitively understand why our method works.Theoretically, we motivate our approach by showing that the weighted loss minimized by simple models using our weighting upper bounds the loss of the complex model.", "target": ["给出一个（准确的）复杂模型，改善简单模型性能的方法。", "本文提出了一种改进低容量模型预测的方法，显示出比现有方法更多的好处。"]}
+{"source": "We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.Our method produces a sequence of feature maps, iteratively refining the SVM margin.We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.", "target": ["利用傅里叶分析和遗憾最小化的工具，从训练数据中学习边际最大化的平移不变量或球形对称核的简单实用算法。", "本文提出在傅里叶表示中学习一个自定义的平移或旋转不变的内核，以使SVM的边际最大化。", "作者提出了一种有趣的算���，用于一起学习l1-SVM和傅里叶表示的核", "作者考虑为机器学习应用直接学习移位/平移不变核的傅里叶表示，将核与数据的一致性作为目标函数来优化。"]}
+{"source": "We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.We show how this can be implemented natively in probabilistic programming.By considering the structure of the counterfactual query, one can significantly optimise the inference process.We also consider design choices to enable further optimisations.We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning.We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.", "target": ["本质上支持因果、反事实推理的概率编程"]}
+{"source": "We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions.We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP.This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning.Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.", "target": ["通过均值场博弈和马尔科夫决策过程的综合，推断大群体行为的均值场博弈（MFG）模型。", "作者通过使用反强化学习来学习模型中代理的奖励函数来处理集体行为模型中的推理。"]}
+{"source": "We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as  offensive basketball gameplay.  When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables.  Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulable way.We present a hierarchical framework that can effectively learn such sequential generative models.  Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime.In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods.We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.", "target": ["我们将深度生成模型与程序化的弱监督相融合，以生成质量明显高于以往基线的协调的多代理轨迹。", "提出了多代理顺序生成模型。", "本文提出了训练生成模型，使用启发式函数产生多代理轨迹，这些函数标记了在训练数据中本来是潜在的变量。"]}
+{"source": "Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations.In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training.In contrast to existing methods, we consider this task as a ranking and transfer learning problem.We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other data sets, our model is able to effectively rank learning curves without having to observe many or very long learning curves.We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture.In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.", "target": ["学会对学习曲线进行排序，以便及早停止没有前途的训练工作。新颖之处：使用成对的排序损失来直接模拟跨数据集的改进和迁移学习的概率，以减少所需训练数据。", "本文提出了一种对神经网络的学习曲线进行排序的方法，可以对不同数据集的学习曲线进行建模，在图像分类任务上实现更高的速度提升。"]}
+{"source": "Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster.Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions.This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills.We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence.Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience.While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning.We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities.When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.", "target": ["我们表明，在持续学习的环境中，通过将非策略性RL应用于新的和重放的经验的混合物，以及行为上的克隆损失，可以避免灾难性的遗忘。", "提出了一种特殊的经验重放与行为克隆的变体，作为一种持续学习的方法。"]}
+{"source": "We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states.We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.", "target": ["我们提出了一种方法，它可以学习在互动代理的背景下整合时间信息和模糊的视觉信息。", "作者提出了图形VRNN，它通过为每个代理部署一个VRNN来模拟多个代理的互动。", "本文提出了一个基于图形神经网络的架构，该架构经过训练可以直接从像素中定位和模拟环境中的代理的相互作用，并显示了模型在跟踪任务和预测代理位置方面的优势。"]}
+{"source": "In this paper we study the problem of learning the weights of a deep convolutional neural network.We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer.We develop an algorithm for simultaneously learning all the kernels from the training data.Our approach dubbed Deep Tensor Decomposition (DeepTD) is based on a rank-1 tensor decomposition.We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels.We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network.Our numerical experiments demonstrate the effectiveness of DeepTD and verify our theoretical findings.", "target": ["我们考虑一个简化的深度卷积神经网络模型。我们表明，这个网络的所有层都可以通过适当应用张量分解来近似学习。", "为使用一阶张量分解学习深度卷积神经网络提供理论保证。", "本文提出了一种用于深度卷积网络的限制性案例的学习方法，其中各层仅限于非重叠情况，每层只有一个输出通道。", "分析了学习一类非常特殊的CNN的问题：每层由一个过滤器组成，应用于输入的非重叠块。"]}
+{"source": "Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.Based on these results, we articulate the \"lottery ticket hypothesis:\" dense, randomly-initialized, feed-forward networks contain subnetworks (\"winning tickets\") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations.We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10.Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.", "target": ["训练后可以修剪权重的前馈神经网络，在训练前也可以修剪同样的权重", "显示存在可以从头开始训练并具有良好泛化性能的稀疏子网络，并提出了一个未经修剪的、随机初始化的NN包含的子网络，可以从头开始训练并具有类似的泛化精度。", "本文研究了这样一个假设：随机初始化的神经网络包含的子网络收敛速度同样快或更快，并能达到相同或更好的分类精度。"]}
+{"source": "We investigate the difficulties of training sparse neural networks and make new observations about optimization dynamics and the energy landscape within the sparse regime.Recent work of \\citep{Gale2019, Liu2018} has shown that sparse ResNet-50 architectures trained on ImageNet-2012 dataset converge to solutions that are significantly worse than those found by pruning.We show that, despite the failure of optimizers, there is a linear path with a monotonically decreasing objective from the initialization to the ``good'' solution.Additionally, our attempts to find a decreasing objective path from ``bad'' solutions to the ``good'' ones in the sparse subspace fail.However, if we allow the path to traverse the dense subspace, then we consistently find a path between two solutions.These findings suggest traversing extra dimensions may be needed to escape stationary points found in the sparse subspace.", "target": ["在本文中，我们通过在能量景观中做插值实验来强调训练稀疏神经网络的难度"]}
+{"source": "Neural network training depends on the structure of the underlying loss landscape, i.e. local minima, saddle points, flat plateaus, and loss barriers.In relation to the structure of the landscape, we study the permutation symmetry of neurons in each layer of a deep neural network, which gives rise not only to multiple equivalent global minima of the loss function but also to critical points in between partner minima.In a network of $d-1$ hidden layers with $n_k$ neurons in layers $k = 1, \\ldots, d$, we construct continuous paths between equivalent global minima that lead through a `permutation point' where the input and output weight vectors of two neurons in the same hidden layer $k$ collide and interchange.We show that such permutation points are critical points which lie inside high-dimensional subspaces of equal loss, contributing to the global flatness of the landscape.We also find that a permutation point for the exchange of neurons $i$ and $j$ transits into a flat high-dimensional plateau that enables all $n_k!$permutations of neurons in a given layer $k$ at the same loss value. Moreover, we introduce higher-order permutation points by exploiting the hierarchical structure in the loss landscapes of neural networks, and find that the number of $K$-th order permutation points is much larger than the (already huge) number of equivalent global minima -- at least by a polynomial factor of order $K$. In twotasks, we demonstrate numerically with our path finding method that continuous paths between partner minima exist: first, in a toy network with a single hidden layer on a function approximation task and, second, in a multilayer network on the MNIST task. Our geometricapproach yields a lower bound on the number of critical points generated by weight-space symmetries and provides a simple intuitive link between previous theoretical results and numerical observations.", "target": ["神经网络景观中的权重空间对称性产生了无数的鞍座和平坦的高维子空间。", "论文提出了一种低损失的方法，从权重空间对称性的角度研究神经网络中与参数有关的损失函数。"]}
+{"source": "The training of stochastic neural network models with binary ($\\pm1$) weights and activations via continuous surrogate networks is investigated.We derive, using mean field theory, a set of scalar equations describing how input signals propagate through surrogate networks.The equations reveal that depending on the choice of surrogate model, the networks may or may not exhibit an order to chaos transition, and the presence of depth scales that limit the maximum trainable depth.Specifically, in solving the equations for edge of chaos conditions, we show that surrogates derived using the Gaussian local reparameterisation trick have no critical initialisation, whereas a deterministic surrogates based on analytic Gaussian integration do.The theory is applied to a range of binary neuron and weight design choices, such as different neuron noise models, allowing the categorisation of algorithms in terms of their behaviour at initialisation.Moreover, we predict theoretically and confirm numerically, that common weight initialization schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance.This study shows that, contrary to common intuition, the means of the stochastic binary weights should be initialised close to close to $\\pm 1$ for deeper networks to be trainable.", "target": ["信号传播理论应用于二进制网的连续替代；反直觉的初始化；重新参数化的技巧没有帮助", "作者研究了使用连续代用物时二元神经网络的训练动态，研究了网络在初始化时应具有哪些属性以进行最佳训练，并对初始化时的随机权重提出了具体建议。", "对随机二元网络、连续代用品及其训练动态的深入探索，以及对如何初始化权重以获得最佳性能的深刻见解。"]}
+{"source": "Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework.Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence.Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph.Our model is arc-factored and therefore parsing and learning are both tractable.Experiments show our model achieves significant and consistent improvement over the supervised baseline.", "target": ["我们提出了一种基于CRF自动编码器框架的半监督学习语义依赖分析器的方法。", "本文重点讨论了半监督语义依赖分析，使用CRF-自动编码器以半监督的方式训练模型，表明在低资源标记数据任务上的有效性。"]}
+{"source": "For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers.In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently.Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods.DeFINE can be incorporated easily in new or existing sequence models.Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity.On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance.On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters,  achieving comparable performance to state-of-the-art methods with fewer parameters.For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%", "target": ["DeFINE使用一个深度的、分层的、带有新的跳过连接的稀疏网络来有效地学习更好的词嵌入。", "本文介绍了一种新的方法，通过使用低维输入和输出层的跳过连接的分层结构，有效地学习深层的词级表征。"]}
+{"source": "In this paper, we present a reproduction of the paper of Bertinetto et al. [2019] \"Meta-learning with differentiable closed-form solvers\" as part of the ICLR 2019 Reproducibility Challenge.In successfully reproducing the most crucial part of the paper, we reach a performance that is comparable with or superior to the original paper on two benchmarks for several settings.We evaluate new baseline results, using a new dataset presented in the paper.Yet, we also provide multiple remarks and recommendations about reproducibility and comparability.  After we brought our reproducibility work to the authors’ attention, they have updated the original paper on which this work is based and released code as well.Our contributions mainly consist in reproducing the most important results of their original paper, in giving insight in the reproducibility and in providing a first open-source implementation.", "target": ["我们成功地重现了一个用于少样本分类的元学习方法，并给出了与基线的比较意见，该方法通过闭式求解器的解法进行反向传播。"]}
+{"source": "Network pruning has emerged as a powerful technique for reducing the size of deep neural networks.Pruning uncovers high-performance subnetworks by taking a trained dense network and gradually removing unimportant connections.Recently, alternative techniques have emerged for training sparse networks directly without having to train a large dense model beforehand, thereby achieving small memory footprints during both training and inference.These techniques are based on dynamic reallocation of non-zero parameters during training.Thus, they are in effect executing a training-time search for the optimal subnetwork.We investigate a most recent one of these techniques and conduct additional experiments to elucidate its behavior in training sparse deep convolutional networks.Dynamic parameter reallocation converges early during training to a highly trainable subnetwork.We show that neither the structure, nor the initialization of the discovered high-performance subnetwork is sufficient to explain its good performance.Rather, it is the dynamics of parameter reallocation that are responsible for successful learning.Dynamic parameter reallocation thus improves the trainability of deep convolutional networks, playing a similar role as overparameterization, without incurring the memory and computational cost of the latter.", "target": ["动态参数重新分配能够成功地直接训练紧凑的稀疏网络，即使在我们预先知道最佳稀疏网络的情况下，它也发挥着不可或缺的作用。"]}
+{"source": "n this paper we present a thrust in three directions of visual development us- ing supervised and semi-supervised techniques.The first is an implementation of semi-supervised object detection and recognition using the principles of Soft At- tention and Generative Adversarial Networks (GANs).The second and the third are supervised networks that learn basic concepts of spatial locality and quantity respectively using Convolutional Neural Networks (CNNs).The three thrusts to- gether are based on the approach of Experiential Robot Learning, introduced in previous publication.While the results are unripe for implementation, we believe they constitute a stepping stone towards autonomous development of robotic vi- sual modules.", "target": ["3 thrusts serving as stepping stones for robot experiential learning of vision module", "调查了现有图像分类器和物体检测器的性能。"]}
+{"source": "Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies.Here we study convolutional neural network-based acoustic models in the context of automatic speech recognition.Adapting a method proposed by Yosinski et al. [2014], we measure the transferability of each layer between German and English to assess the their language-specifity.We observe three distinct regions of transferability: (1) the first two layers are entirely transferable between languages, (2) layers 2–8 are also highly transferable but we find evidence of some language specificity, (3) the subsequent fully connected layers are more language specific but can be successfully finetuned to the target language.To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [Raghu et al., 2017].Our results are consistent with the observation that CCNs converge 'bottom up' during training and demonstrate the benefit of freeze training, especially for transfer learning.", "target": ["除了基于CNN的声学模型的前两层之外，所有这些模型都表现出一定程度的语言特异性，但冻结训练使其能够在语言之间成功转移。", "本文衡量了基于CNN的声学模型中各层特征在不同语言间的可转移性，结论是用 \"冻结训练 \"技术训练的AM比其他转移的模型表现更好。"]}
+{"source": "Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence.We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region).This is done in the discrete and continuous multi-armed bandit settings with entropy regularisation.We show that in the small steps limit with respect to the Wasserstein distance $W_2$, policy dynamics are governed by the heat equation, following the Jordan-Kinderlehrer-Otto result.This means that policies undergo diffusion and advection, concentrating near actions with high reward.This helps elucidate the nature of convergence in the probability matching setup, and provides justification for empirical practices such as Gaussian policy priors and additive gradient noise.", "target": ["将Wasserstein-信任区的熵策略梯度与热方程联系起来。", "本文探讨了强化学习和二次优化运输理论之间的联系", "作者研究了在多臂强盗背景下，由Wasserstein距离的信任区域限制的策略变化的政策梯度，显示在小步数限制下，策略动态受热方程（Fokker-Planck方程）支配。"]}
+{"source": "The softmax function is widely used to train deep neural networks for multi-class classification.Despite its outstanding performance in classification tasks, the features derived from the supervision of softmax are usually sub-optimal in some scenarios where Euclidean distances apply in feature spaces.To address this issue, we propose a new loss, dubbed the isotropic loss, in the sense that the overall distribution of data points is regularized to approach the isotropic normal one.Combined with the vanilla softmax, we formalize a novel criterion called the isotropic softmax, or isomax for short, for supervised learning of deep neural networks.By virtue of the isomax, the intra-class features are penalized by the isotropic loss while inter-class distances are well kept by the original softmax loss.Moreover, the isomax loss does not require any additional modifications to the network, mini-batches or the training process.Extensive experiments on classification and clustering are performed to demonstrate the superiority and robustness of the isomax loss.", "target": ["通过对数据点的全局分布进行各向同性的归一化处理，有效提高了softmax学习对象特征向量的辨别能力。"]}
+{"source": "A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.Recently,  Jin et al. (2018) proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP.In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \\emph{without} accessing a generative model.We show that the \\textit{sample complexity of exploration} of our algorithm is bounded by $\\tilde{O}({\\frac{SA}{\\epsilon^2(1-\\gamma)^7}})$.This improves the previously best known result of $\\tilde{O}({\\frac{SA}{\\epsilon^4(1-\\gamma)^8}})$ in this setting achieved by delayed Q-learning (Strehlet al., 2006),, and matches the lower bound in terms of $\\epsilon$ as well as $S$ and $A$ up to logarithmic factors.", "target": ["我们将带有UCB探索奖励的Q-learning调整为无限范围的MDP，无需访问生成模型即可获得折扣奖励，并改进了之前已知的最佳结果。", "本文考虑了一种带有UCB探索策略的Q-learning算法，用于无限期的MDP。"]}
+{"source": "Backpropagation is driving today's artificial neural networks (ANNs).However, despite extensive research, it remains unclear if the brain implements this algorithm.Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient.However, the convergence rate of such learning scales poorly with the number of involved neurons.Here we propose a hybrid learning approach.Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide.We provide proof that our approach converges to the true gradient for certain classes of networks.In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning.Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.", "target": ["扰动可用于训练反馈权重，以便在全连接和卷积神经网络中学习。", "本文提出了一种方法，通过使用基于噪声的估计器来估计后向通道的权重，解决 \"权重传递 \"问题 "]}
+{"source": "This paper proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, 0/1 error etc.) evaluated for a model arising at (any point of) a training run.This pattern is universal in the sense that this one to one relationship is identical across architectures (such as VGG, Resnet, Densenet etc.), algorithms (SGD and SGD with momentum) and training loss functions (cross entropy and mean squared error).", "target": ["我们在训练神经网络时，在不同的代用损失（CE、MSE、0-1损失）的行为中发现了一些普遍的模式（即在不同的架构中保持不变），并提出了支持的实证证据。"]}
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